diff --git a/.Rbuildignore b/.Rbuildignore index 43ea4e01..08cb3270 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -15,3 +15,4 @@ ^\.github$ ^Meta$ ^revdep$ +^CODE_OF_CONDUCT\.md$ diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..54408753 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,126 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at hebbali.aravind@gmail.com. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion]. + +For answers to common questions about this code of conduct, see the FAQ at +. Translations are available at . + +[homepage]: https://www.contributor-covenant.org diff --git a/DESCRIPTION b/DESCRIPTION index 0822e057..1b3f8112 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: blorr Type: Package Title: Tools for Developing Binary Logistic Regression Models -Version: 0.3.1 +Version: 0.3.1.9000 Authors@R: person("Aravind", "Hebbali", email = "hebbali.aravind@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9220-9669")) Description: Tools designed to make it easier for beginner and intermediate users to build and validate diff --git a/NEWS.md b/NEWS.md index 90063d3d..b2c2b7c1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# blorr 0.3.1.9000 + # blorr 0.3.1 This is a patch release to fix minor bugs. diff --git a/README.Rmd b/README.Rmd index 0f4915e8..fbc23fde 100644 --- a/README.Rmd +++ b/README.Rmd @@ -144,3 +144,6 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, use [StackOverflow](https://stackoverflow.com/). +## Code of Conduct + +Please note that the blorr project is released with a [Contributor Code of Conduct](https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms. diff --git a/README.md b/README.md index 092bf642..24833182 100644 --- a/README.md +++ b/README.md @@ -271,3 +271,9 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, use [StackOverflow](https://stackoverflow.com/). + +## Code of Conduct + +Please note that the blorr project is released with a [Contributor Code +of Conduct](https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html). By +contributing to this project, you agree to abide by its terms. diff --git a/_pkgdown.yml b/_pkgdown.yml index 902b263e..8f9d69cc 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,6 @@ url: https://blorr.rsquaredacademy.com +template: + bootstrap: 5 authors: Aravind Hebbali: @@ -6,11 +8,7 @@ authors: templates: params: - bootswatch: cosmo - ganalytics: UA-57270671-33 - docsearch: - api_key: - index_name: blorr + bootswatch: cerulean navbar: title: "blorr" @@ -34,13 +32,16 @@ navbar: - icon: fa-github href: https://github.com/rsquaredacademy/blorr - home: links: - - text: Learn more - href: https://www.rsquaredacademy.com - - text: Try shiny app - href: https://www.rsquaredcomputing.com/shiny/blorr/ + - text: Our R packages + href: https://pkgs.rsquaredacademy.com + - text: Read our blog + href: https://blog.rsquaredacademy.com + - text: Our ebooks + href: https://ebooks.rsquaredacademy.com + - text: Online courses + href: https://www.rsquaredacademy.com reference: - title: Shiny App diff --git a/docs/404.html b/docs/404.html index e9fe9c08..7f8929ec 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,166 +1,84 @@ - - - - + + + + - - + Page not found (404) • blorr + + + + + + + + Skip to contents - - - - - - - - - - - - - - - - - - - - - - - - - - - +
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Content not found. Please use links in the navbar. -
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- + - - diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html new file mode 100644 index 00000000..c2102cd1 --- /dev/null +++ b/docs/CODE_OF_CONDUCT.html @@ -0,0 +1,128 @@ + +Contributor Covenant Code of Conduct • blorr + Skip to contents + + +
+
+
+ +

Contributor Covenant Code of Conduct

+ Source: CODE_OF_CONDUCT.md +
+ +
+ +
+

Our Pledge

+

We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.

+

We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.

+
+
+

Our Standards

+

Examples of behavior that contributes to a positive environment for our community include:

+
  • Demonstrating empathy and kindness toward other people
    • +
  • Being respectful of differing opinions, viewpoints, and experiences
    • +
  • Giving and gracefully accepting constructive feedback
    • +
  • Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
    • +
  • Focusing on what is best not just for us as individuals, but for the overall community
    • +

Examples of unacceptable behavior include:

+
  • The use of sexualized language or imagery, and sexual attention or advances of any kind
    • +
  • Trolling, insulting or derogatory comments, and personal or political attacks
    • +
  • Public or private harassment
    • +
  • Publishing others’ private information, such as a physical or email address, without their explicit permission
    • +
  • Other conduct which could reasonably be considered inappropriate in a professional setting
    • +
+
+

Enforcement Responsibilities

+

Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.

+

Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.

+
+
+

Scope

+

This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.

+
+
+

Enforcement

+

Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at . All complaints will be reviewed and investigated promptly and fairly.

+

All community leaders are obligated to respect the privacy and security of the reporter of any incident.

+
+
+

Enforcement Guidelines

+

Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:

+
+

1. Correction

+

Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.

+

Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.

+
+
+

2. Warning

+

Community Impact: A violation through a single incident or series of actions.

+

Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.

+
+
+

3. Temporary Ban

+

Community Impact: A serious violation of community standards, including sustained inappropriate behavior.

+

Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.

+
+
+

4. Permanent Ban

+

Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.

+

Consequence: A permanent ban from any sort of public interaction within the community.

+
+
+
+

Attribution

+

This Code of Conduct is adapted from the Contributor Covenant, version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.

+

Community Impact Guidelines were inspired by [Mozilla’s code of conduct enforcement ladder][https://github.com/mozilla/inclusion].

+

For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.

+
+
+ +
+ + +
+ + + + + + + diff --git a/docs/CONDUCT.html b/docs/CONDUCT.html index 92a33b47..5f6b4dab 100644 --- a/docs/CONDUCT.html +++ b/docs/CONDUCT.html @@ -1,134 +1,41 @@ - - - - - - +Contributor Code of Conduct • blorr + Skip to contents -Contributor Code of Conduct • blorr +
-
-
+
+

Contributor Code of Conduct

+ Source: CONDUCT.md
@@ -138,37 +45,25 @@

Contributor Code of Conduct

Examples of unacceptable behavior by participants include the use of sexual language or imagery, derogatory comments or personal attacks, trolling, public or private harassment, insults, or other unprofessional conduct.

Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed from the project team.

Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by opening an issue or contacting one or more of the project maintainers.

-

This Code of Conduct is adapted from the Contributor Covenant (http:contributor-covenant.org), version 1.0.0, available at http://contributor-covenant.org/version/1/0/0/

+

This Code of Conduct is adapted from the Contributor Covenant (http:contributor-covenant.org), version 1.0.0, available at http://contributor-covenant.org/version/1/0/0/

-
+
- +
- -
- - - + diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 5b19ffab..c80b3b96 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -1,168 +1,63 @@ - - - - - - +License • blorr + Skip to contents -License • blorr +
-
-
+
+

License

+ 
YEAR: 2017
 COPYRIGHT HOLDER: Aravind Hebbali
-
+
- +
- -
- - - + diff --git a/docs/LICENSE.html b/docs/LICENSE.html index e3ebd270..6c650436 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -1,134 +1,41 @@ - - - - - - +MIT License • blorr + Skip to contents -MIT License • blorr +
-
-
+
+

MIT License

+ Source: LICENSE.md
@@ -139,34 +46,22 @@

MIT License

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

-
+
- +
- -
- - - + diff --git a/docs/articles/index.html b/docs/articles/index.html index 9ed64925..8db21f27 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,165 +1,65 @@ - - - - - - +Articles • blorr + Skip to contents -Articles • blorr +
-
-
+
+

Articles

All vignettes

-

+
-
-
A Short Introduction to the blorr Package
-
-
-
-
-
+
A Short Introduction to the blorr Package
+
+
+
- + - - - + diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index d045fec3..f78c6d74 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -4,750 +4,826 @@ - + A Short Introduction to the blorr Package • blorr - - - - - - - - + + + + + - -
-
-
-
-

A Short Introduction to the blorr Package

-

Aravind Hebbali

+ +
+
+ +

A Short Introduction to the blorr Package

+

Aravind +Hebbali

- Source: vignettes/introduction.Rmd - - + Source: vignettes/introduction.Rmd +
introduction.Rmd
-
-

-Introduction

-

The blorr package offers tools for building and validating binary logistic regression models. It is most suitable for beginner/intermediate R users and those who teach statistics using R. The API is very simple and most of the functions take either a data.frame/tibble or a model as input. blorr use consistent prefix blr_ for easy tab completion.

-
-

-Installation

+
+

Introduction +

+

The blorr package offers tools for building and validating binary +logistic regression models. It is most suitable for +beginner/intermediate R users and those who teach statistics using R. +The API is very simple and most of the functions take either a +data.frame/tibble or a model as +input. blorr use consistent prefix +blr_ for easy tab completion.

+
+

Installation +

You can install blorr using:

-

The documentation of the package can be found at https://blorr.rsquaredacademy.com. This vignette gives a quick tour of the package.

+install.packages("blorr")
+

The documentation of the package can be found at https://blorr.rsquaredacademy.com. This vignette gives a +quick tour of the package.

-
-

-Libraries

+
+

Libraries +

The following libraries are used in the examples in the vignette:

-
-
-
-

-Data

-

To demonstrate the features of blorr, we will use the bank marketing data set. The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (‘yes’) or not (‘no’) subscribed. It contains a random sample (~4k) of the original data set which can be found at https://archive.ics.uci.edu/ml/datasets/bank+marketing.

-
-
-

-Bivariate Analysis

-

Let us begin with careful bivariate analysis of each possible variable and the outcome variable. We will use information value and likelihood ratio chi square test for selecting the initial set of predictors for our model. The bivariate analysis is currently avialable for categorical predictors only.

+library(blorr) +library(magrittr)
+
+
+
+

Data +

+

To demonstrate the features of blorr, we will use the bank marketing +data set. The data is related with direct marketing campaigns of a +Portuguese banking institution. The marketing campaigns were based on +phone calls. Often, more than one contact to the same client was +required, in order to access if the product (bank term deposit) would be +(‘yes’) or not (‘no’) subscribed. It contains a random sample (~4k) of +the original data set which can be found at https://archive.ics.uci.edu/ml/datasets/bank+marketing.

+
+
+

Bivariate Analysis +

+

Let us begin with careful bivariate analysis of each possible +variable and the outcome variable. We will use information value and +likelihood ratio chi square test for selecting the initial set of +predictors for our model. The bivariate analysis is currently avialable +for categorical predictors only.

-blr_bivariate_analysis(bank_marketing, y, job, marital, education, default, 
-  housing, loan, contact, poutcome)
-#>                           Bivariate Analysis                           
-#> ----------------------------------------------------------------------
-#> Variable     Information Value    LR Chi Square    LR DF    LR p-value 
-#> ----------------------------------------------------------------------
-#>    job             0.16              75.2690        11        0.0000   
-#>  marital           0.05              21.6821         2        0.0000   
-#> education          0.05              25.0466         3        0.0000   
-#>  default           0.02              6.0405          1        0.0140   
-#>  housing           0.16              72.2813         1        0.0000   
-#>   loan             0.06              26.6615         1        0.0000   
-#>  contact           0.31             124.3834         2        0.0000   
-#> poutcome           0.53             270.6450         3        0.0000   
-#> ----------------------------------------------------------------------
-
-

-Weight of Evidence & Information Value

-

Weight of evidence (WoE) is used to assess the relative risk of di¤erent attributes for a characteristic and as a means to transform characteristics into variables. It is also a very useful tool for binning. The WoE for any group with average odds is zero. A negative WoE indicates that the proportion of defaults is higher for that attribute than the overall proportion and indicates higher risk.

-

The information value is used to rank order variables in terms of their predictive power. A high information value indicates a high ability to discriminate. Values for the information value will always be positive and may be above 3 when assessing highly predictive characteristics. Characteristics with information values less than 0:10 are typically viewed as weak, while values over 0.30 are sought after.

+blr_bivariate_analysis(bank_marketing, y, job, marital, education, default, + housing, loan, contact, poutcome) +#> Bivariate Analysis +#> ---------------------------------------------------------------------- +#> Variable Information Value LR Chi Square LR DF LR p-value +#> ---------------------------------------------------------------------- +#> job 0.16 75.2690 11 0.0000 +#> marital 0.05 21.6821 2 0.0000 +#> education 0.05 25.0466 3 0.0000 +#> default 0.02 6.0405 1 0.0140 +#> housing 0.16 72.2813 1 0.0000 +#> loan 0.06 26.6615 1 0.0000 +#> contact 0.31 124.3834 2 0.0000 +#> poutcome 0.53 270.6450 3 0.0000 +#> ----------------------------------------------------------------------
+
+

Weight of Evidence & Information Value +

+

Weight of evidence (WoE) is used to assess the relative risk of +di¤erent attributes for a characteristic and as a means to transform +characteristics into variables. It is also a very useful tool for +binning. The WoE for any group with average odds is zero. A negative WoE +indicates that the proportion of defaults is higher for that attribute +than the overall proportion and indicates higher risk.

+

The information value is used to rank order variables in terms of +their predictive power. A high information value indicates a high +ability to discriminate. Values for the information value will always be +positive and may be above 3 when assessing highly predictive +characteristics. Characteristics with information values less than 0:10 +are typically viewed as weak, while values over 0.30 are sought +after.

-blr_woe_iv(bank_marketing, job, y)
-#>                                Weight of Evidence                                
-#> --------------------------------------------------------------------------------
-#>    levels        count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> --------------------------------------------------------------------------------
-#>  management        809         130          0.20       0.25      -0.22     0.01  
-#>  technician        682          79          0.17       0.15       0.11     0.00  
-#> entrepreneur       139          12          0.03       0.02       0.40     0.00  
-#>  blue-collar       937          73          0.23       0.14       0.51     0.05  
-#>    unknown          29          2           0.01       0.00       0.61     0.00  
-#>    retired         152          47          0.04       0.09      -0.87     0.05  
-#>    admin.          433          61          0.11       0.12      -0.09     0.00  
-#>   services         392          39          0.10       0.08       0.26     0.01  
-#> self-employed      132          22          0.03       0.04      -0.26     0.00  
-#>  unemployed        126          15          0.03       0.03       0.08     0.00  
-#>   housemaid        110          12          0.03       0.02       0.17     0.00  
-#>    student          63          25          0.02       0.05      -1.13     0.04  
-#> --------------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   job            0.1594       
-#> -----------------------------
-
-

-Plot

+blr_woe_iv(bank_marketing, job, y) +#> Weight of Evidence +#> -------------------------------------------------------------------------------- +#> levels count_0s count_1s dist_0s dist_1s woe iv +#> -------------------------------------------------------------------------------- +#> management 809 130 0.20 0.25 -0.22 0.01 +#> technician 682 79 0.17 0.15 0.11 0.00 +#> entrepreneur 139 12 0.03 0.02 0.40 0.00 +#> blue-collar 937 73 0.23 0.14 0.51 0.05 +#> unknown 29 2 0.01 0.00 0.61 0.00 +#> retired 152 47 0.04 0.09 -0.87 0.05 +#> admin. 433 61 0.11 0.12 -0.09 0.00 +#> services 392 39 0.10 0.08 0.26 0.01 +#> self-employed 132 22 0.03 0.04 -0.26 0.00 +#> unemployed 126 15 0.03 0.03 0.08 0.00 +#> housemaid 110 12 0.03 0.02 0.17 0.00 +#> student 63 25 0.02 0.05 -1.13 0.04 +#> -------------------------------------------------------------------------------- +#> +#> Information Value +#> ----------------------------- +#> Variable Information Value +#> ----------------------------- +#> job 0.1594 +#> -----------------------------
+
+

Plot +

-k <- blr_woe_iv(bank_marketing, job, y)
-plot(k)
+k <- blr_woe_iv(bank_marketing, job, y) +plot(k)

-
-

-Multiple Variables

-

We can generate the weight of evidence and information value for multiple variables using blr_woe_iv_stats().

+
+

Multiple Variables +

+

We can generate the weight of evidence and information value for +multiple variables using blr_woe_iv_stats().

-blr_woe_iv_stats(bank_marketing, y, job, marital, education)
-#> Variable: job
-#> 
-#>                                Weight of Evidence                                
-#> --------------------------------------------------------------------------------
-#>    levels        count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> --------------------------------------------------------------------------------
-#>  management        809         130          0.20       0.25      -0.22     0.01  
-#>  technician        682          79          0.17       0.15       0.11     0.00  
-#> entrepreneur       139          12          0.03       0.02       0.40     0.00  
-#>  blue-collar       937          73          0.23       0.14       0.51     0.05  
-#>    unknown          29          2           0.01       0.00       0.61     0.00  
-#>    retired         152          47          0.04       0.09      -0.87     0.05  
-#>    admin.          433          61          0.11       0.12      -0.09     0.00  
-#>   services         392          39          0.10       0.08       0.26     0.01  
-#> self-employed      132          22          0.03       0.04      -0.26     0.00  
-#>  unemployed        126          15          0.03       0.03       0.08     0.00  
-#>   housemaid        110          12          0.03       0.02       0.17     0.00  
-#>    student          63          25          0.02       0.05      -1.13     0.04  
-#> --------------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   job            0.1594       
-#> -----------------------------
-#> 
-#> 
-#> Variable: marital
-#> 
-#>                             Weight of Evidence                              
-#> ---------------------------------------------------------------------------
-#>  levels     count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> ---------------------------------------------------------------------------
-#> married       2467        273          0.62       0.53       0.15     0.01  
-#>  single       1079        191          0.27       0.37      -0.32     0.03  
-#> divorced      458          53          0.11       0.10       0.11     0.00  
-#> ---------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#> marital          0.0464       
-#> -----------------------------
-#> 
-#> 
-#> Variable: education
-#> 
-#>                              Weight of Evidence                              
-#> ----------------------------------------------------------------------------
-#>  levels      count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> ----------------------------------------------------------------------------
-#> tertiary       1104        195          0.28       0.38      -0.31     0.03  
-#> secondary      2121        231          0.53       0.45       0.17     0.01  
-#>  unknown       154          25          0.04       0.05      -0.23     0.00  
-#>  primary       625          66          0.16       0.13       0.20     0.01  
-#> ----------------------------------------------------------------------------
-#> 
-#>       Information Value        
-#> ------------------------------
-#> Variable     Information Value 
-#> ------------------------------
-#> education         0.0539       
-#> ------------------------------
-

blr_woe_iv() and blr_woe_iv_stats() are currently avialable for categorical predictors only.

-
-
-
-
-

-Stepwise Selection

-

For the initial/ first cut model, all the independent variables are put into the model. Our goal is to include a limited number of independent variables (5-15) which are all significant, without sacrificing too much on the model performance. The rationale behind not-including too many variables is that the model would be over fitted and would become unstable when tested on the validation sample. The variable reduction is done using forward or backward or stepwise variable selection procedures. We will use blr_step_aic_both() to shortlist predictors for our model.

-
-

-Model

+blr_woe_iv_stats(bank_marketing, y, job, marital, education) +#> Variable: job +#> +#> Weight of Evidence +#> -------------------------------------------------------------------------------- +#> levels count_0s count_1s dist_0s dist_1s woe iv +#> -------------------------------------------------------------------------------- +#> management 809 130 0.20 0.25 -0.22 0.01 +#> technician 682 79 0.17 0.15 0.11 0.00 +#> entrepreneur 139 12 0.03 0.02 0.40 0.00 +#> blue-collar 937 73 0.23 0.14 0.51 0.05 +#> unknown 29 2 0.01 0.00 0.61 0.00 +#> retired 152 47 0.04 0.09 -0.87 0.05 +#> admin. 433 61 0.11 0.12 -0.09 0.00 +#> services 392 39 0.10 0.08 0.26 0.01 +#> self-employed 132 22 0.03 0.04 -0.26 0.00 +#> unemployed 126 15 0.03 0.03 0.08 0.00 +#> housemaid 110 12 0.03 0.02 0.17 0.00 +#> student 63 25 0.02 0.05 -1.13 0.04 +#> -------------------------------------------------------------------------------- +#> +#> Information Value +#> ----------------------------- +#> Variable Information Value +#> ----------------------------- +#> job 0.1594 +#> ----------------------------- +#> +#> +#> Variable: marital +#> +#> Weight of Evidence +#> --------------------------------------------------------------------------- +#> levels count_0s count_1s dist_0s dist_1s woe iv +#> --------------------------------------------------------------------------- +#> married 2467 273 0.62 0.53 0.15 0.01 +#> single 1079 191 0.27 0.37 -0.32 0.03 +#> divorced 458 53 0.11 0.10 0.11 0.00 +#> --------------------------------------------------------------------------- +#> +#> Information Value +#> ----------------------------- +#> Variable Information Value +#> ----------------------------- +#> marital 0.0464 +#> ----------------------------- +#> +#> +#> Variable: education +#> +#> Weight of Evidence +#> ---------------------------------------------------------------------------- +#> levels count_0s count_1s dist_0s dist_1s woe iv +#> ---------------------------------------------------------------------------- +#> tertiary 1104 195 0.28 0.38 -0.31 0.03 +#> secondary 2121 231 0.53 0.45 0.17 0.01 +#> unknown 154 25 0.04 0.05 -0.23 0.00 +#> primary 625 66 0.16 0.13 0.20 0.01 +#> ---------------------------------------------------------------------------- +#> +#> Information Value +#> ------------------------------ +#> Variable Information Value +#> ------------------------------ +#> education 0.0539 +#> ------------------------------
+

blr_woe_iv() and blr_woe_iv_stats() are +currently avialable for categorical predictors only.

+
+
+
+
+

Stepwise Selection +

+

For the initial/ first cut model, all the independent variables are +put into the model. Our goal is to include a limited number of +independent variables (5-15) which are all significant, without +sacrificing too much on the model performance. The rationale behind +not-including too many variables is that the model would be over fitted +and would become unstable when tested on the validation sample. The +variable reduction is done using forward or backward or stepwise +variable selection procedures. We will use +blr_step_aic_both() to shortlist predictors for our +model.

+
+

Model +

-model <- glm(y ~ ., data = bank_marketing, family = binomial(link = 'logit'))
-
-

-Selection Summary

+model <- glm(y ~ ., data = bank_marketing, family = binomial(link = 'logit'))
+
+

Selection Summary +

-blr_step_aic_both(model)
-#> Stepwise Selection Method 
-#> -------------------------
-#> 
-#> Candidate Terms: 
-#> 
-#> 1 . age 
-#> 2 . job 
-#> 3 . marital 
-#> 4 . education 
-#> 5 . default 
-#> 6 . balance 
-#> 7 . housing 
-#> 8 . loan 
-#> 9 . contact 
-#> 10 . day 
-#> 11 . month 
-#> 12 . duration 
-#> 13 . campaign 
-#> 14 . pdays 
-#> 15 . previous 
-#> 16 . poutcome 
-#> 
-#> 
-#> Variables Entered/Removed: 
-#> 
-#> - duration added 
-#> - poutcome added 
-#> - month added 
-#> - contact added 
-#> - housing added 
-#> - loan added 
-#> - campaign added 
-#> - marital added 
-#> - education added 
-#> - age added 
-#> 
-#> No more variables to be added or removed.
-#> 
-#> 
-#>                      Stepwise Summary                      
-#> ---------------------------------------------------------
-#> Variable      Method       AIC         BIC       Deviance 
-#> ---------------------------------------------------------
-#> duration     addition    2674.384    2687.217    2670.384 
-#> poutcome     addition    2396.014    2428.097    2386.014 
-#> month        addition    2274.109    2376.773    2242.109 
-#> contact      addition    2207.884    2323.381    2171.884 
-#> housing      addition    2184.550    2306.463    2146.550 
-#> loan         addition    2171.972    2300.302    2131.972 
-#> campaign     addition    2164.164    2298.910    2122.164 
-#> marital      addition    2158.524    2306.103    2112.524 
-#> education    addition    2155.837    2322.666    2103.837 
-#> age          addition    2154.272    2327.517    2100.272 
-#> ---------------------------------------------------------
-
-
-

-Plot

+blr_step_aic_both(model) +#> Stepwise Selection Method +#> ------------------------- +#> +#> Candidate Terms: +#> +#> 1 . age +#> 2 . job +#> 3 . marital +#> 4 . education +#> 5 . default +#> 6 . balance +#> 7 . housing +#> 8 . loan +#> 9 . contact +#> 10 . day +#> 11 . month +#> 12 . duration +#> 13 . campaign +#> 14 . pdays +#> 15 . previous +#> 16 . poutcome +#> +#> +#> Variables Entered/Removed: +#> +#> - duration added +#> - poutcome added +#> - month added +#> - contact added +#> - housing added +#> - loan added +#> - campaign added +#> - marital added +#> - education added +#> - age added +#> +#> No more variables to be added or removed. +#> +#> +#> Stepwise Summary +#> --------------------------------------------------------- +#> Variable Method AIC BIC Deviance +#> --------------------------------------------------------- +#> duration addition 2674.384 2687.217 2670.384 +#> poutcome addition 2396.014 2428.097 2386.014 +#> month addition 2274.109 2376.773 2242.109 +#> contact addition 2207.884 2323.381 2171.884 +#> housing addition 2184.550 2306.463 2146.550 +#> loan addition 2171.972 2300.302 2131.972 +#> campaign addition 2164.164 2298.910 2122.164 +#> marital addition 2158.524 2306.103 2112.524 +#> education addition 2155.837 2322.666 2103.837 +#> age addition 2154.272 2327.517 2100.272 +#> ---------------------------------------------------------
+
+
+

Plot +

-model %>%
-  blr_step_aic_both() %>%
-  plot()
-#> Stepwise Selection Method 
-#> -------------------------
-#> 
-#> Candidate Terms: 
-#> 
-#> 1 . age 
-#> 2 . job 
-#> 3 . marital 
-#> 4 . education 
-#> 5 . default 
-#> 6 . balance 
-#> 7 . housing 
-#> 8 . loan 
-#> 9 . contact 
-#> 10 . day 
-#> 11 . month 
-#> 12 . duration 
-#> 13 . campaign 
-#> 14 . pdays 
-#> 15 . previous 
-#> 16 . poutcome 
-#> 
-#> 
-#> Variables Entered/Removed: 
-#> 
-#> - duration added 
-#> - poutcome added 
-#> - month added 
-#> - contact added 
-#> - housing added 
-#> - loan added 
-#> - campaign added 
-#> - marital added 
-#> - education added 
-#> - age added 
-#> 
-#> No more variables to be added or removed.
+model %>% + blr_step_aic_both() %>% + plot() +#> Stepwise Selection Method +#> ------------------------- +#> +#> Candidate Terms: +#> +#> 1 . age +#> 2 . job +#> 3 . marital +#> 4 . education +#> 5 . default +#> 6 . balance +#> 7 . housing +#> 8 . loan +#> 9 . contact +#> 10 . day +#> 11 . month +#> 12 . duration +#> 13 . campaign +#> 14 . pdays +#> 15 . previous +#> 16 . poutcome +#> +#> +#> Variables Entered/Removed: +#> +#> - duration added +#> - poutcome added +#> - month added +#> - contact added +#> - housing added +#> - loan added +#> - campaign added +#> - marital added +#> - education added +#> - age added +#> +#> No more variables to be added or removed.

-
-

-Regression Output

-
-

-Model

-

We can use bivariate analysis and stepwise selection procedure to shortlist predictors and build the model using the glm(). The predictors used in the below model are for illustration purposes and not necessarily shortlisted from the bivariate analysis and variable selection procedures.

+
+

Regression Output +

+
+

Model +

+

We can use bivariate analysis and stepwise selection procedure to +shortlist predictors and build the model using the glm(). +The predictors used in the below model are for illustration purposes and +not necessarily shortlisted from the bivariate analysis and variable +selection procedures.

-model <- glm(y ~  age + duration + previous + housing + default +
-             loan + poutcome + job + marital, data = bank_marketing, 
-             family = binomial(link = 'logit'))
-

Use blr_regress() to generate comprehensive regression output. It accepts either of the following

+model <- glm(y ~ age + duration + previous + housing + default + + loan + poutcome + job + marital, data = bank_marketing, + family = binomial(link = 'logit'))
+

Use blr_regress() to generate comprehensive regression +output. It accepts either of the following

    -
  • model built using glm() +
  • model built using glm()
  • model formula and data
-
-

-Using Model

-

Let us look at the output generated from blr_regress():

+
+

Using Model +

+

Let us look at the output generated from +blr_regress():

-blr_regress(model)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data         y        4521      4520           4498           TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0             4004              1              517    
-#> --------------------------------------------------------
-#> 
-#>                      Maximum Likelihood Estimates                       
-#> -----------------------------------------------------------------------
-#>    Parameter        DF    Estimate    Std. Error    z value     Pr(>|z|) 
-#> -----------------------------------------------------------------------
-#>   (Intercept)       1     -5.1347        0.3728    -13.7729      0.0000 
-#>       age           1      0.0096        0.0067      1.4299      0.1528 
-#>     duration        1      0.0042         2e-04     20.7853      0.0000 
-#>     previous        1     -0.0357        0.0392     -0.9089      0.3634 
-#>    housingno        1      0.7894        0.1232      6.4098      0.0000 
-#>    defaultyes       1     -0.8691        0.6919     -1.2562      0.2091 
-#>      loanno         1      0.6598        0.1945      3.3925       7e-04 
-#> poutcomefailure     1      0.6085        0.2012      3.0248      0.0025 
-#>  poutcomeother      1      1.1354        0.2700      4.2057      0.0000 
-#> poutcomesuccess     1      3.2481        0.2462     13.1913      0.0000 
-#>  jobtechnician      1     -0.2713        0.1806     -1.5019      0.1331 
-#> jobentrepreneur     1     -0.7041        0.3809     -1.8486      0.0645 
-#>  jobblue-collar     1     -0.6132        0.1867     -3.2851      0.0010 
-#>    jobunknown       1     -0.9932        0.8226     -1.2073      0.2273 
-#>    jobretired       1      0.3197        0.2729      1.1713      0.2415 
-#>    jobadmin.        1      0.1120        0.2001      0.5599      0.5755 
-#>   jobservices       1     -0.1750        0.2265     -0.7728      0.4397 
-#> jobself-employed    1     -0.1408        0.3009     -0.4680      0.6398 
-#>  jobunemployed      1     -0.6581        0.3432     -1.9174      0.0552 
-#>   jobhousemaid      1     -0.7456        0.3932     -1.8963      0.0579 
-#>    jobstudent       1      0.1927        0.3433      0.5613      0.5746 
-#>  maritalsingle      1      0.5451        0.1387      3.9299       1e-04 
-#> maritaldivorced     1     -0.1989        0.1986     -1.0012      0.3167 
-#> -----------------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8886          Somers' D        0.7773   
-#> % Discordant          0.1114          Gamma            0.7773   
-#> % Tied                0.0000          Tau-a            0.1575   
-#> Pairs                2070068          c                0.8886   
-#> ---------------------------------------------------------------
-

If you want to examine the odds ratio estimates, set odd_conf_limit to TRUE. The odds ratio estimates are not explicitly computed as we observed considerable increase in computation time when dealing with large data sets.

-
-
-

-Using Formula

-

Let us use the model formula and the data set to generate the above results.

+blr_regress(model) +#> Model Overview +#> ------------------------------------------------------------------------ +#> Data Set Resp Var Obs. Df. Model Df. Residual Convergence +#> ------------------------------------------------------------------------ +#> data y 4521 4520 4498 TRUE +#> ------------------------------------------------------------------------ +#> +#> Response Summary +#> -------------------------------------------------------- +#> Outcome Frequency Outcome Frequency +#> -------------------------------------------------------- +#> 0 4004 1 517 +#> -------------------------------------------------------- +#> +#> Maximum Likelihood Estimates +#> ----------------------------------------------------------------------- +#> Parameter DF Estimate Std. Error z value Pr(>|z|) +#> ----------------------------------------------------------------------- +#> (Intercept) 1 -5.1347 0.3728 -13.7729 0.0000 +#> age 1 0.0096 0.0067 1.4299 0.1528 +#> duration 1 0.0042 2e-04 20.7853 0.0000 +#> previous 1 -0.0357 0.0392 -0.9089 0.3634 +#> housingno 1 0.7894 0.1232 6.4098 0.0000 +#> defaultyes 1 -0.8691 0.6919 -1.2562 0.2091 +#> loanno 1 0.6598 0.1945 3.3925 7e-04 +#> poutcomefailure 1 0.6085 0.2012 3.0248 0.0025 +#> poutcomeother 1 1.1354 0.2700 4.2057 0.0000 +#> poutcomesuccess 1 3.2481 0.2462 13.1913 0.0000 +#> jobtechnician 1 -0.2713 0.1806 -1.5019 0.1331 +#> jobentrepreneur 1 -0.7041 0.3809 -1.8486 0.0645 +#> jobblue-collar 1 -0.6132 0.1867 -3.2851 0.0010 +#> jobunknown 1 -0.9932 0.8226 -1.2073 0.2273 +#> jobretired 1 0.3197 0.2729 1.1713 0.2415 +#> jobadmin. 1 0.1120 0.2001 0.5599 0.5755 +#> jobservices 1 -0.1750 0.2265 -0.7728 0.4397 +#> jobself-employed 1 -0.1408 0.3009 -0.4680 0.6398 +#> jobunemployed 1 -0.6581 0.3432 -1.9174 0.0552 +#> jobhousemaid 1 -0.7456 0.3932 -1.8963 0.0579 +#> jobstudent 1 0.1927 0.3433 0.5613 0.5746 +#> maritalsingle 1 0.5451 0.1387 3.9299 1e-04 +#> maritaldivorced 1 -0.1989 0.1986 -1.0012 0.3167 +#> ----------------------------------------------------------------------- +#> +#> Association of Predicted Probabilities and Observed Responses +#> --------------------------------------------------------------- +#> % Concordant 0.8886 Somers' D 0.7773 +#> % Discordant 0.1114 Gamma 0.7773 +#> % Tied 0.0000 Tau-a 0.1575 +#> Pairs 2070068 c 0.8886 +#> ---------------------------------------------------------------
+

If you want to examine the odds ratio estimates, set +odd_conf_limit to TRUE. The odds ratio +estimates are not explicitly computed as we observed considerable +increase in computation time when dealing with large data sets.

+
+
+

Using Formula +

+

Let us use the model formula and the data set to generate the above +results.

-blr_regress(y ~  age + duration + previous + housing + default +
-             loan + poutcome + job + marital, data = bank_marketing)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data         y        4521      4520           4498           TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0             4004              1              517    
-#> --------------------------------------------------------
-#> 
-#>                      Maximum Likelihood Estimates                       
-#> -----------------------------------------------------------------------
-#>    Parameter        DF    Estimate    Std. Error    z value     Pr(>|z|) 
-#> -----------------------------------------------------------------------
-#>   (Intercept)       1     -5.1347        0.3728    -13.7729      0.0000 
-#>       age           1      0.0096        0.0067      1.4299      0.1528 
-#>     duration        1      0.0042         2e-04     20.7853      0.0000 
-#>     previous        1     -0.0357        0.0392     -0.9089      0.3634 
-#>    housingno        1      0.7894        0.1232      6.4098      0.0000 
-#>    defaultyes       1     -0.8691        0.6919     -1.2562      0.2091 
-#>      loanno         1      0.6598        0.1945      3.3925       7e-04 
-#> poutcomefailure     1      0.6085        0.2012      3.0248      0.0025 
-#>  poutcomeother      1      1.1354        0.2700      4.2057      0.0000 
-#> poutcomesuccess     1      3.2481        0.2462     13.1913      0.0000 
-#>  jobtechnician      1     -0.2713        0.1806     -1.5019      0.1331 
-#> jobentrepreneur     1     -0.7041        0.3809     -1.8486      0.0645 
-#>  jobblue-collar     1     -0.6132        0.1867     -3.2851      0.0010 
-#>    jobunknown       1     -0.9932        0.8226     -1.2073      0.2273 
-#>    jobretired       1      0.3197        0.2729      1.1713      0.2415 
-#>    jobadmin.        1      0.1120        0.2001      0.5599      0.5755 
-#>   jobservices       1     -0.1750        0.2265     -0.7728      0.4397 
-#> jobself-employed    1     -0.1408        0.3009     -0.4680      0.6398 
-#>  jobunemployed      1     -0.6581        0.3432     -1.9174      0.0552 
-#>   jobhousemaid      1     -0.7456        0.3932     -1.8963      0.0579 
-#>    jobstudent       1      0.1927        0.3433      0.5613      0.5746 
-#>  maritalsingle      1      0.5451        0.1387      3.9299       1e-04 
-#> maritaldivorced     1     -0.1989        0.1986     -1.0012      0.3167 
-#> -----------------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8886          Somers' D        0.7773   
-#> % Discordant          0.1114          Gamma            0.7773   
-#> % Tied                0.0000          Tau-a            0.1575   
-#> Pairs                2070068          c                0.8886   
-#> ---------------------------------------------------------------
-
-
-
-
-

-Model Fit Statistics

-

Model fit statistics are available to assess how well the model fits the data and to compare two different models.The output includes likelihood ratio test, AIC, BIC and a host of pseudo r-squared measures. You can read more about pseudo r-squared at https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/.

-
-

-Single Model

+blr_regress(y ~ age + duration + previous + housing + default + + loan + poutcome + job + marital, data = bank_marketing) +#> Model Overview +#> ------------------------------------------------------------------------ +#> Data Set Resp Var Obs. Df. Model Df. Residual Convergence +#> ------------------------------------------------------------------------ +#> data y 4521 4520 4498 TRUE +#> ------------------------------------------------------------------------ +#> +#> Response Summary +#> -------------------------------------------------------- +#> Outcome Frequency Outcome Frequency +#> -------------------------------------------------------- +#> 0 4004 1 517 +#> -------------------------------------------------------- +#> +#> Maximum Likelihood Estimates +#> ----------------------------------------------------------------------- +#> Parameter DF Estimate Std. Error z value Pr(>|z|) +#> ----------------------------------------------------------------------- +#> (Intercept) 1 -5.1347 0.3728 -13.7729 0.0000 +#> age 1 0.0096 0.0067 1.4299 0.1528 +#> duration 1 0.0042 2e-04 20.7853 0.0000 +#> previous 1 -0.0357 0.0392 -0.9089 0.3634 +#> housingno 1 0.7894 0.1232 6.4098 0.0000 +#> defaultyes 1 -0.8691 0.6919 -1.2562 0.2091 +#> loanno 1 0.6598 0.1945 3.3925 7e-04 +#> poutcomefailure 1 0.6085 0.2012 3.0248 0.0025 +#> poutcomeother 1 1.1354 0.2700 4.2057 0.0000 +#> poutcomesuccess 1 3.2481 0.2462 13.1913 0.0000 +#> jobtechnician 1 -0.2713 0.1806 -1.5019 0.1331 +#> jobentrepreneur 1 -0.7041 0.3809 -1.8486 0.0645 +#> jobblue-collar 1 -0.6132 0.1867 -3.2851 0.0010 +#> jobunknown 1 -0.9932 0.8226 -1.2073 0.2273 +#> jobretired 1 0.3197 0.2729 1.1713 0.2415 +#> jobadmin. 1 0.1120 0.2001 0.5599 0.5755 +#> jobservices 1 -0.1750 0.2265 -0.7728 0.4397 +#> jobself-employed 1 -0.1408 0.3009 -0.4680 0.6398 +#> jobunemployed 1 -0.6581 0.3432 -1.9174 0.0552 +#> jobhousemaid 1 -0.7456 0.3932 -1.8963 0.0579 +#> jobstudent 1 0.1927 0.3433 0.5613 0.5746 +#> maritalsingle 1 0.5451 0.1387 3.9299 1e-04 +#> maritaldivorced 1 -0.1989 0.1986 -1.0012 0.3167 +#> ----------------------------------------------------------------------- +#> +#> Association of Predicted Probabilities and Observed Responses +#> --------------------------------------------------------------- +#> % Concordant 0.8886 Somers' D 0.7773 +#> % Discordant 0.1114 Gamma 0.7773 +#> % Tied 0.0000 Tau-a 0.1575 +#> Pairs 2070068 c 0.8886 +#> ---------------------------------------------------------------
+
+
+
+
+

Model Fit Statistics +

+

Model fit statistics are available to assess how well the model fits +the data and to compare two different models.The output includes +likelihood ratio test, AIC, BIC and a host of pseudo r-squared measures. +You can read more about pseudo r-squared here.

+
+

Single Model +

-blr_model_fit_stats(model)
-#>                                Model Fit Statistics                                
-#> ----------------------------------------------------------------------------------
-#> Log-Lik Intercept Only:     -1607.330    Log-Lik Full Model:             -1123.340 
-#> Deviance(4498):              2246.679    LR(22):                           967.980 
-#>                                          Prob > LR:                          0.000 
-#> MCFadden's R2                   0.301    McFadden's Adj R2:                  0.287 
-#> ML (Cox-Snell) R2:              0.193    Cragg-Uhler(Nagelkerke) R2:         0.379 
-#> McKelvey & Zavoina's R2:        0.388    Efron's R2:                         0.278 
-#> Count R2:                       0.904    Adj Count R2:                       0.157 
-#> BIC:                         2440.259    AIC:                             2292.679 
-#> ----------------------------------------------------------------------------------
-
-
-
-

-Model Validation

-
-

-Confusion Matrix

-

In the event of deciding a cut-off point on the probability scores of a logistic regression model, a confusion matrix is created corresponding to a particular cut-off. The observations with probability scores above the cut-off score are predicted to be events and those below the cut-off score, as non-events. The confusion matrix, a 2X2 table, then calculates the number of correctly classified and miss-classified observations.

+blr_model_fit_stats(model) +#> Model Fit Statistics +#> ---------------------------------------------------------------------------------- +#> Log-Lik Intercept Only: -1607.330 Log-Lik Full Model: -1123.340 +#> Deviance(4498): 2246.679 LR(22): 967.980 +#> Prob > LR: 0.000 +#> MCFadden's R2 0.301 McFadden's Adj R2: 0.287 +#> ML (Cox-Snell) R2: 0.193 Cragg-Uhler(Nagelkerke) R2: 0.379 +#> McKelvey & Zavoina's R2: 0.388 Efron's R2: 0.278 +#> Count R2: 0.904 Adj Count R2: 0.157 +#> BIC: 2440.259 AIC: 2292.679 +#> ----------------------------------------------------------------------------------
+
+ +
+

Model Validation +

+
+

Confusion Matrix +

+

In the event of deciding a cut-off point on the probability scores of +a logistic regression model, a confusion matrix is created corresponding +to a particular cut-off. The observations with probability scores above +the cut-off score are predicted to be events and those below the cut-off +score, as non-events. The confusion matrix, a 2X2 table, then calculates +the number of correctly classified and miss-classified observations.

-blr_confusion_matrix(model, cutoff = 0.5)
-#> Confusion Matrix and Statistics 
-#> 
-#>           Reference
-#> Prediction    0    1
-#>          0 3920  352
-#>          1   84  165
-#> 
-#> 
-#>                 Accuracy : 0.9036 
-#>      No Information Rate : 0.8856 
-#> 
-#>                    Kappa : 0.3851 
-#> 
-#> McNemars's Test P-Value  : 0.0000 
-#> 
-#>              Sensitivity : 0.3191 
-#>              Specificity : 0.9790 
-#>           Pos Pred Value : 0.6627 
-#>           Neg Pred Value : 0.9176 
-#>               Prevalence : 0.1144 
-#>           Detection Rate : 0.0365 
-#>     Detection Prevalence : 0.0551 
-#>        Balanced Accuracy : 0.6491 
-#>                Precision : 0.6627 
-#>                   Recall : 0.3191 
-#> 
-#>         'Positive' Class : 1
-

The validity of a cut-off is measured using sensitivity, specificity and accuracy.

+blr_confusion_matrix(model, cutoff = 0.5) +#> Confusion Matrix and Statistics +#> +#> Reference +#> Prediction 0 1 +#> 0 3920 352 +#> 1 84 165 +#> +#> +#> Accuracy : 0.9036 +#> No Information Rate : 0.8856 +#> +#> Kappa : 0.3851 +#> +#> McNemars's Test P-Value : 0.0000 +#> +#> Sensitivity : 0.3191 +#> Specificity : 0.9790 +#> Pos Pred Value : 0.6627 +#> Neg Pred Value : 0.9176 +#> Prevalence : 0.1144 +#> Detection Rate : 0.0365 +#> Detection Prevalence : 0.0551 +#> Balanced Accuracy : 0.6491 +#> Precision : 0.6627 +#> Recall : 0.3191 +#> +#> 'Positive' Class : 1
+

The validity of a cut-off is measured using sensitivity, specificity +and accuracy.

    -

  • Sensitivity: The % of correctly classified events out of all events = TP / (TP + FN)

    • -

  • Specificity: The % of correctly classified non-events out of all non-events = TN / (TN + FP)

    • -

  • Accuracy: The % of correctly classified observation over all observations = (TP + TN) / (TP + FP + TN + FN)

    • -

  • True Positive (TP) : Events correctly classified as events.

    • -

  • True Negative (TN) : Non-Events correctly classified as non-events.

    • -

  • False Positive (FP): Non-events miss-classified as events.

    • -

  • False Negative (FN): Events miss-classified as non-events.

    • +

  • Sensitivity: The % of correctly classified +events out of all events = TP / (TP + FN)

    • +

  • Specificity: The % of correctly classified +non-events out of all non-events = TN / (TN + FP)

    • +

  • Accuracy: The % of correctly classified +observation over all observations = (TP + TN) / (TP + FP + TN + +FN)

    • +

  • True Positive (TP) : Events correctly classified as +events.

    • +

  • True Negative (TN) : Non-Events correctly classified as +non-events.

    • +

  • False Positive (FP): Non-events miss-classified as +events.

    • +

  • False Negative (FN): Events miss-classified as +non-events.

-

For a standard logistic model, the higher is the cut-off, the lower will be the sensitivity and the higher would be the specificity. As the cut-off is decreased, sensitivity will go up, as then more events would be captured. Also, specificity will go down, as more non-events would miss-classified as events. Hence a trade-off is done based on the requirements. For example, if we are looking to capture as many events as possible, and we can afford to have miss-classified non-events, then a low cut-off is taken.

-
-
-

-Hosmer Lemeshow Test

-

Hosmer and Lemeshow developed a goodness-of-fit test for logistic regression models with binary responses. The test involves dividing the data into approximately ten groups of roughly equal size based on the percentiles of the estimated probabilities. The observations are sorted in increasing order of their estimated probability of having an even outcome. The discrepancies between the observed and expected number of observations in these groups are summarized by the Pearson chi-square statistic, which is then compared to chi-square distribution with t degrees of freedom, where t is the number of groups minus 2. Lower values of Goodness-of-fit are preferred.

+

For a standard logistic model, the higher is the cut-off, the lower +will be the sensitivity and the higher would be the specificity. As the +cut-off is decreased, sensitivity will go up, as then more events would +be captured. Also, specificity will go down, as more non-events would +miss-classified as events. Hence a trade-off is done based on the +requirements. For example, if we are looking to capture as many events +as possible, and we can afford to have miss-classified non-events, then +a low cut-off is taken.

+
+
+

Hosmer Lemeshow Test +

+

Hosmer and Lemeshow developed a goodness-of-fit test for logistic +regression models with binary responses. The test involves dividing the +data into approximately ten groups of roughly equal size based on the +percentiles of the estimated probabilities. The observations are sorted +in increasing order of their estimated probability of having an even +outcome. The discrepancies between the observed and expected number of +observations in these groups are summarized by the Pearson chi-square +statistic, which is then compared to chi-square distribution with t +degrees of freedom, where t is the number of groups minus 2. Lower +values of Goodness-of-fit are preferred.

-blr_test_hosmer_lemeshow(model)
-#>            Partition for the Hosmer & Lemeshow Test            
-#> --------------------------------------------------------------
-#>                         def = 1                 def = 0        
-#> Group    Total    Observed    Expected    Observed    Expected 
-#> --------------------------------------------------------------
-#>   1       453        2          5.14        451        447.86  
-#>   2       452        3          8.63        449        443.37  
-#>   3       452        4         11.88        448        440.12  
-#>   4       452        7         15.29        445        436.71  
-#>   5       452        14        19.39        438        432.61  
-#>   6       452        10        24.97        442        427.03  
-#>   7       452        31        33.65        421        418.35  
-#>   8       452        62        49.74        390        402.26  
-#>   9       452       128        88.10        324        363.90  
-#>  10       452       256        260.21       196        191.79  
-#> --------------------------------------------------------------
-#> 
-#>      Goodness of Fit Test      
-#> ------------------------------
-#> Chi-Square    DF    Pr > ChiSq 
-#> ------------------------------
-#>  52.9942      8       0.0000   
-#> ------------------------------
-
-
-

-Gains Table & Lift Chart

-

A lift curve is a graphical representation of the % of cumulative events captured at a specific cut-off. The cut-off can be a particular decile or a percentile. Similar, to rank ordering procedure, the data is in descending order of the scores and is then grouped into deciles/percentiles. The cumulative number of observations and events are then computed for each decile/percentile. The lift curve is the created using the cumulative % population as the x-axis and the cumulative percentage of events as the y-axis.

+blr_test_hosmer_lemeshow(model) +#> Partition for the Hosmer & Lemeshow Test +#> -------------------------------------------------------------- +#> def = 1 def = 0 +#> Group Total Observed Expected Observed Expected +#> -------------------------------------------------------------- +#> 1 453 2 5.14 451 447.86 +#> 2 452 3 8.63 449 443.37 +#> 3 452 4 11.88 448 440.12 +#> 4 452 7 15.29 445 436.71 +#> 5 452 14 19.39 438 432.61 +#> 6 452 10 24.97 442 427.03 +#> 7 452 31 33.65 421 418.35 +#> 8 452 62 49.74 390 402.26 +#> 9 452 128 88.10 324 363.90 +#> 10 452 256 260.21 196 191.79 +#> -------------------------------------------------------------- +#> +#> Goodness of Fit Test +#> ------------------------------ +#> Chi-Square DF Pr > ChiSq +#> ------------------------------ +#> 52.9942 8 0.0000 +#> ------------------------------
+ +
+

Gains Table & Lift Chart +

+

A lift curve is a graphical representation of the % of cumulative +events captured at a specific cut-off. The cut-off can be a particular +decile or a percentile. Similar, to rank ordering procedure, the data is +in descending order of the scores and is then grouped into +deciles/percentiles. The cumulative number of observations and events +are then computed for each decile/percentile. The lift curve is the +created using the cumulative % population as the x-axis and the +cumulative percentage of events as the y-axis.

-blr_gains_table(model)
-#>    decile total   1   0       ks  tp   tn   fp  fn sensitivity specificity
-#> 1       1   452 256 196 44.62134 256 3808  196 261    49.51644    95.10490
-#> 2       2   452 128 324 61.28765 384 3484  520 133    74.27466    87.01299
-#> 3       3   452  62 390 63.53965 446 3094  910  71    86.26692    77.27273
-#> 4       4   452  31 421 59.02130 477 2673 1331  40    92.26306    66.75824
-#> 5       5   452  10 442 49.91657 487 2231 1773  30    94.19729    55.71928
-#> 6       6   452  14 438 41.68544 501 1793 2211  16    96.90522    44.78022
-#> 7       7   452   7 445 31.92552 508 1348 2656   9    98.25919    33.66633
-#> 8       8   452   4 448 21.51040 512  900 3104   5    99.03288    22.47752
-#> 9       9   452   3 449 10.87689 515  451 3553   2    99.61315    11.26374
-#> 10     10   453   2 451  0.00000 517    0 4004   0   100.00000     0.00000
-#>    accuracy
-#> 1  89.89162
-#> 2  85.55629
-#> 3  78.30126
-#> 4  69.67485
-#> 5  60.11944
-#> 6  50.74099
-#> 7  41.05286
-#> 8  31.23203
-#> 9  21.36695
-#> 10 11.43552
-
-

-Lift Chart

+blr_gains_table(model) +#> decile total 1 0 ks tp tn fp fn sensitivity specificity +#> 1 1 452 256 196 44.62134 256 3808 196 261 49.51644 95.10490 +#> 2 2 452 128 324 61.28765 384 3484 520 133 74.27466 87.01299 +#> 3 3 452 62 390 63.53965 446 3094 910 71 86.26692 77.27273 +#> 4 4 452 31 421 59.02130 477 2673 1331 40 92.26306 66.75824 +#> 5 5 452 10 442 49.91657 487 2231 1773 30 94.19729 55.71928 +#> 6 6 452 14 438 41.68544 501 1793 2211 16 96.90522 44.78022 +#> 7 7 452 7 445 31.92552 508 1348 2656 9 98.25919 33.66633 +#> 8 8 452 4 448 21.51040 512 900 3104 5 99.03288 22.47752 +#> 9 9 452 3 449 10.87689 515 451 3553 2 99.61315 11.26374 +#> 10 10 453 2 451 0.00000 517 0 4004 0 100.00000 0.00000 +#> accuracy +#> 1 89.89162 +#> 2 85.55629 +#> 3 78.30126 +#> 4 69.67485 +#> 5 60.11944 +#> 6 50.74099 +#> 7 41.05286 +#> 8 31.23203 +#> 9 21.36695 +#> 10 11.43552
+
+

Lift Chart +

-model %>%
-    blr_gains_table() %>%
-    plot()
+model %>% + blr_gains_table() %>% + plot()

-
-

-ROC Curve

-

ROC curve is a graphical representation of the validity of cut-offs for a logistic regression model. The ROC curve is plotted using the sensitivity and specificity for all possible cut-offs, i.e., all the probability scores. The graph is plotted using sensitivity on the y-axis and 1-specificity on the x-axis. Any point on the ROC curve represents a sensitivity X (1-specificity) measure corresponding to a cut-off. The area under the ROC curve is used as a validation measure for the model – the bigger the area the better is the model.

+
+

ROC Curve +

+

ROC curve is a graphical representation of the validity of cut-offs +for a logistic regression model. The ROC curve is plotted using the +sensitivity and specificity for all possible cut-offs, i.e., all the +probability scores. The graph is plotted using sensitivity on the y-axis +and 1-specificity on the x-axis. Any point on the ROC curve represents a +sensitivity X (1-specificity) measure corresponding to a cut-off. The +area under the ROC curve is used as a validation measure for the model – +the bigger the area the better is the model.

+model %>% + blr_gains_table() %>% + blr_roc_curve()

-
-

-KS Chart

-

The KS Statistic is again a measure of model efficiency, and it is created using the lift curve. The lift curve is created to plot % events. If we also plot % non-events on the same scale, with % population at x-axis, we would get another curve. The maximum distance between the lift curve for events and that for non-events is termed as KS. For a good model, KS should be big (>=0.3) and should occur as close to the event rate as possible.

+
+

KS Chart +

+

The KS Statistic is again a measure of model efficiency, and it is +created using the lift curve. The lift curve is created to plot % +events. If we also plot % non-events on the same scale, with % +population at x-axis, we would get another curve. The maximum distance +between the lift curve for events and that for non-events is termed as +KS. For a good model, KS should be big (>=0.3) and should occur as +close to the event rate as possible.

+model %>% + blr_gains_table() %>% + blr_ks_chart()

-
-

-Decile Lift Chart

-

The decile lift chart displays the lift over the global mean event rate for each decile. For a model with good discriminatory power, the top deciles should have an event/conversion rate greater than the global mean.

+
+

Decile Lift Chart +

+

The decile lift chart displays the lift over the global mean event +rate for each decile. For a model with good discriminatory power, the +top deciles should have an event/conversion rate greater than the global +mean.

+model %>% + blr_gains_table() %>% + blr_decile_lift_chart()

-
-

-Capture Rate by Decile

-

If the model has good discriminatory power, the top deciles should have a higher event/conversion rate compared to the bottom deciles.

+
+

Capture Rate by Decile +

+

If the model has good discriminatory power, the top deciles should +have a higher event/conversion rate compared to the bottom deciles.

+model %>% + blr_gains_table() %>% + blr_decile_capture_rate()

-
-

-Lorenz Curve

-

The Lorenz curve is a simple graphic device which illustrates the degree of inequality in the distribution of thevariable concerned. It is a visual representation of inequality used to measure the discriminatory power of the predictive model.

+
+

Lorenz Curve +

+

The Lorenz curve is a simple graphic device which illustrates the +degree of inequality in the distribution of thevariable concerned. It is +a visual representation of inequality used to measure the discriminatory +power of the predictive model.

+blr_lorenz_curve(model)

-
-

-Residual & Influence Diagnostics

-

blorr can generate 22 plots for residual, influence and leverage diagnostics.

-
-

-Influence Diagnostics

+
+

Residual & Influence Diagnostics +

+

blorr can generate 22 plots for residual, influence +and leverage diagnostics.

+
+

Influence Diagnostics +

+blr_plot_diag_influence(model)

-
-

-Leverage Diagnostics

+
+

Leverage Diagnostics +

+blr_plot_diag_leverage(model)

-
-

-Fitted Values Diagnostics

+
+

Fitted Values Diagnostics +

+blr_plot_diag_fit(model)

-
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+
+
- -
-
- - - - -
- -
-
-
-

Authors

+
+

Authors

+ +
+ +
+

Citation

+

Source: DESCRIPTION

+ +

Hebbali A (2024). +blorr: Tools for Developing Binary Logistic Regression Models. +R package version 0.3.1.9000, https://github.com/rsquaredacademy/blorr, https://blorr.rsquaredacademy.com/. +

+ 
@Manual{,
+  title = {blorr: Tools for Developing Binary Logistic Regression Models},
+  author = {Aravind Hebbali},
+  year = {2024},
+  note = {R package version 0.3.1.9000, https://github.com/rsquaredacademy/blorr},
+  url = {https://blorr.rsquaredacademy.com/},
+}
- +
-
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hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return 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sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n return\n }\n\n instanceMap.set(key, instance)\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null\n }\n\n return null\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return\n }\n\n const instanceMap = elementMap.get(element)\n\n instanceMap.delete(key)\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element)\n }\n }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n }\n\n return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID)\n } while (document.getElementById(prefix))\n\n return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0\n }\n\n // Get transition-duration of the element\n let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n const floatTransitionDuration = Number.parseFloat(transitionDuration)\n const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0]\n transitionDelay = transitionDelay.split(',')[0]\n\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0]\n }\n\n return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object))\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getjQuery,\n getNextActiveElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n parseSelector,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n return null\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n }\n\n return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n },\n\n getSelectorFromElement(element) {\n const selector = getSelector(element)\n\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null\n }\n\n return null\n },\n\n getElementFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.findOne(selector) : null\n },\n\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.find(selector) : []\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getElement,\n reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n execute,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n EventHandler.off(window, EVENT_KEY)\n EventHandler.off(this._dialog, EVENT_KEY)\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n defineJQueryPlugin,\n isDisabled,\n isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this])\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
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',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper()\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n this._popper = this._createPopper(tip)\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n this._disposePopper()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // TODO: remove this check in v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element])\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element])\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
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' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and
')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('');n.attr("href","#"+e),n.text(t);var r=a("
  • ");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1 + + + + + + + + + + + + diff --git a/docs/deps/font-awesome-6.4.2/css/all.css b/docs/deps/font-awesome-6.4.2/css/all.css new file mode 100644 index 00000000..bdb6e3ae --- /dev/null +++ b/docs/deps/font-awesome-6.4.2/css/all.css @@ -0,0 +1,7968 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + 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content: "\f0a8"; } + +.fa-group-arrows-rotate::before { + content: "\e4f6"; } + +.fa-bowl-food::before { + content: "\e4c6"; } + +.fa-candy-cane::before { + content: "\f786"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-cloud-bolt::before { + content: "\f76c"; } + +.fa-thunderstorm::before { + content: "\f76c"; } + +.fa-text-slash::before { + content: "\f87d"; } + +.fa-remove-format::before { + content: "\f87d"; } + +.fa-face-smile-wink::before { + content: "\f4da"; } + +.fa-smile-wink::before { + content: "\f4da"; } + +.fa-file-word::before { + content: "\f1c2"; } + +.fa-file-powerpoint::before { + content: "\f1c4"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-house-lock::before { + content: "\e510"; } + +.fa-cloud-arrow-down::before { + content: "\f0ed"; } + 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content: "\f3ed"; } + +.fa-shield-alt::before { + content: "\f3ed"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-envelope-circle-check::before { + content: "\e4e8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-heart-circle-check::before { + content: "\e4fd"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + +.fa-file-zipper::before { + content: "\f1c6"; } + +.fa-file-archive::before { + content: "\f1c6"; } + +.fa-square::before { + content: "\f0c8"; } + +.fa-martini-glass-empty::before { + content: "\f000"; } + +.fa-glass-martini::before { + content: "\f000"; } + +.fa-couch::before { + content: "\f4b8"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-italic::before { + content: "\f033"; } + 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} + +.fa-locust::before { + content: "\e520"; } + +.fa-sort::before { + content: "\f0dc"; } + +.fa-unsorted::before { + content: "\f0dc"; } + +.fa-list-ol::before { + content: "\f0cb"; } + +.fa-list-1-2::before { + content: "\f0cb"; } + +.fa-list-numeric::before { + content: "\f0cb"; } + +.fa-person-dress-burst::before { + content: "\e544"; } + +.fa-money-check-dollar::before { + content: "\f53d"; } + +.fa-money-check-alt::before { + content: "\f53d"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-bread-slice::before { + content: "\f7ec"; } + +.fa-language::before { + content: "\f1ab"; } + +.fa-face-kiss-wink-heart::before { + content: "\f598"; } + +.fa-kiss-wink-heart::before { + content: "\f598"; } + +.fa-filter::before { + content: "\f0b0"; } + +.fa-question::before { + content: "\3f"; } + +.fa-file-signature::before { + content: "\f573"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + 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content: "\e0a9"; } + +.fa-f::before { + content: "\46"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-road::before { + content: "\f018"; } + +.fa-taxi::before { + content: "\f1ba"; } + +.fa-cab::before { + content: "\f1ba"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-chart-pie::before { + content: "\f200"; } + +.fa-pie-chart::before { + content: "\f200"; } + +.fa-bolt-lightning::before { + content: "\e0b7"; } + +.fa-sack-xmark::before { + content: "\e56a"; } + +.fa-file-excel::before { + content: "\f1c3"; } + +.fa-file-contract::before { + content: "\f56c"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-building-flag::before { + content: "\e4d5"; } + +.fa-face-grin-beam::before { + content: "\f582"; } + +.fa-grin-beam::before { + content: "\f582"; } + +.fa-object-ungroup::before { + content: "\f248"; } + +.fa-poop::before { + content: "\f619"; } + +.fa-location-pin::before { + content: "\f041"; } + +.fa-map-marker::before { + content: "\f041"; } + +.fa-kaaba::before { + content: "\f66b"; } + +.fa-toilet-paper::before { + content: "\f71e"; } + +.fa-helmet-safety::before { + content: "\f807"; } + +.fa-hard-hat::before { + content: "\f807"; } + +.fa-hat-hard::before { + content: "\f807"; } + +.fa-eject::before { + content: "\f052"; } + +.fa-circle-right::before { + content: "\f35a"; } + +.fa-arrow-alt-circle-right::before { + content: "\f35a"; } + +.fa-plane-circle-check::before { + content: "\e555"; } + +.fa-face-rolling-eyes::before { + content: "\f5a5"; } + +.fa-meh-rolling-eyes::before { + content: "\f5a5"; } + +.fa-object-group::before { + content: "\f247"; } + +.fa-chart-line::before { + content: "\f201"; } + +.fa-line-chart::before { + content: "\f201"; } + +.fa-mask-ventilator::before { + content: "\e524"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-signs-post::before { + content: "\f277"; } + +.fa-map-signs::before { + content: "\f277"; } + +.fa-cash-register::before { + content: "\f788"; } + 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content: "\f885"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-circle-chevron-left::before { + content: "\f137"; } + +.fa-chevron-circle-left::before { + content: "\f137"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tents::before { + content: "\e582"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + 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{ + content: "\f234"; } + +.fa-check::before { + content: "\f00c"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + 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+.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + 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+.fa.fa-thumb-tack:before { + content: "\f08d"; } + +.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; 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Hide your header until you need it + * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery); \ No newline at end of file diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js new file mode 100644 index 00000000..fc6c299b --- /dev/null +++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
    " ], + col: [ 2, "", "
    " ], + tr: [ 2, "", "
    " ], + td: [ 3, "", "
    " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " - - - - - - - +blorr • Tools for Developing Binary Logistic Regression Models + + + + + + + + + + + + + + + + + + + + + + + + + + + - -
    -
    -
    -
    -

    -blorr

    +
    +
    +
    +
    +

    blorr +

    Tools for building binary logistic regression models

    -

    CRAN_Status_Badge cran checks r-universeR build status Lifecycle: stable Coverage status status

    -
    -

    -Overview

    + + +
    +

    Overview +

    Tools designed to make it easier for users, particularly beginner/intermediate R users to build logistic regression models. Includes comprehensive regression output, variable selection procedures, model validation techniques and a ‘shiny’ app for interactive model building.

    -
    -

    -Installation

    +
    +

    Installation +

    -# Install blorr from CRAN
-install.packages("blorr")
-
-# Install development version from GitHub
-# install.packages("devtools")
-devtools::install_github("rsquaredacademy/blorr")
-
-# Install the development version from `rsquaredacademy` universe
-install.packages("blorr", repos = "https://rsquaredacademy.r-universe.dev")
    +# Install blorr from CRAN +install.packages("blorr") + +# Install development version from GitHub +# install.packages("devtools") +devtools::install_github("rsquaredacademy/blorr") + +# Install the development version from `rsquaredacademy` universe +install.packages("blorr", repos = "https://rsquaredacademy.r-universe.dev")
    -
    -

    -Articles

    +
    +

    Articles +

    -
    -

    -Usage

    +
    +

    Usage +

    blorr uses consistent prefix blr_* for easy tab completion.

    -
    -

    -Bivariate Analysis

    +library(blorr) +library(magrittr)
    +
    +

    Bivariate Analysis +

    -blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp)
-#>                          Bivariate Analysis                           
-#> ---------------------------------------------------------------------
-#> Variable    Information Value    LR Chi Square    LR DF    LR p-value 
-#> ---------------------------------------------------------------------
-#>  female           0.10              3.9350          1        0.0473   
-#>   prog            0.43              16.1450         2        3e-04    
-#>   race            0.33              11.3694         3        0.0099   
-#>  schtyp           0.00              0.0445          1        0.8330   
-#> ---------------------------------------------------------------------
    +blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp) +#> Bivariate Analysis +#> --------------------------------------------------------------------- +#> Variable Information Value LR Chi Square LR DF LR p-value +#> --------------------------------------------------------------------- +#> female 0.10 3.9350 1 0.0473 +#> prog 0.43 16.1450 2 3e-04 +#> race 0.33 11.3694 3 0.0099 +#> schtyp 0.00 0.0445 1 0.8330 +#> ---------------------------------------------------------------------
    -
    -

    -Weight of Evidence & Information Value

    +
    +

    Weight of Evidence & Information Value +

    -blr_woe_iv(hsb2, prog, honcomp)
-#>                            Weight of Evidence                             
-#> -------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> -------------------------------------------------------------------------
-#>   1          38          7           0.26       0.13       0.67     0.08  
-#>   2          65          40          0.44       0.75      -0.53     0.17  
-#>   3          44          6           0.30       0.11       0.97     0.18  
-#> -------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   prog           0.4329       
-#> -----------------------------
    +blr_woe_iv(hsb2, prog, honcomp) +#> Weight of Evidence +#> ------------------------------------------------------------------------- +#> levels count_0s count_1s dist_0s dist_1s woe iv +#> ------------------------------------------------------------------------- +#> 1 38 7 0.26 0.13 0.67 0.08 +#> 2 65 40 0.44 0.75 -0.53 0.17 +#> 3 44 6 0.30 0.11 0.97 0.18 +#> ------------------------------------------------------------------------- +#> +#> Information Value +#> ----------------------------- +#> Variable Information Value +#> ----------------------------- +#> prog 0.4329 +#> -----------------------------
    -
    -

    -Model

    +
    +

    Model +

    -# create model using glm
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
    +# create model using glm +model <- glm(honcomp ~ female + read + science, data = hsb2, + family = binomial(link = 'logit'))
    -
    -

    -Regression Output

    +
    +

    Regression Output +

    -blr_regress(model)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data      honcomp     200        199           196            TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0              147              1              53     
-#> --------------------------------------------------------
-#> 
-#>                   Maximum Likelihood Estimates                    
-#> -----------------------------------------------------------------
-#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
-#> -----------------------------------------------------------------
-#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
-#>   female1      1      1.4825        0.4474     3.3139       9e-04 
-#>    read        1      0.1035        0.0258     4.0186       1e-04 
-#>   science      1      0.0948        0.0305     3.1129      0.0019 
-#> -----------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8561          Somers' D        0.7147   
-#> % Discordant          0.1425          Gamma            0.7136   
-#> % Tied                0.0014          Tau-a            0.2794   
-#> Pairs                  7791           c                0.8568   
-#> ---------------------------------------------------------------
    +blr_regress(model) +#> Model Overview +#> ------------------------------------------------------------------------ +#> Data Set Resp Var Obs. Df. Model Df. Residual Convergence +#> ------------------------------------------------------------------------ +#> data honcomp 200 199 196 TRUE +#> ------------------------------------------------------------------------ +#> +#> Response Summary +#> -------------------------------------------------------- +#> Outcome Frequency Outcome Frequency +#> -------------------------------------------------------- +#> 0 147 1 53 +#> -------------------------------------------------------- +#> +#> Maximum Likelihood Estimates +#> ----------------------------------------------------------------- +#> Parameter DF Estimate Std. Error z value Pr(>|z|) +#> ----------------------------------------------------------------- +#> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 +#> female1 1 1.4825 0.4474 3.3139 9e-04 +#> read 1 0.1035 0.0258 4.0186 1e-04 +#> science 1 0.0948 0.0305 3.1129 0.0019 +#> ----------------------------------------------------------------- +#> +#> Association of Predicted Probabilities and Observed Responses +#> --------------------------------------------------------------- +#> % Concordant 0.8561 Somers' D 0.7147 +#> % Discordant 0.1425 Gamma 0.7136 +#> % Tied 0.0014 Tau-a 0.2794 +#> Pairs 7791 c 0.8568 +#> ---------------------------------------------------------------
    -
    -

    -Model Fit Statistics

    +
    +

    Model Fit Statistics +

    -blr_model_fit_stats(model)
-#>                               Model Fit Statistics                                
-#> ---------------------------------------------------------------------------------
-#> Log-Lik Intercept Only:      -115.644    Log-Lik Full Model:              -80.118 
-#> Deviance(196):                160.236    LR(3):                            71.052 
-#>                                          Prob > LR:                         0.000 
-#> MCFadden's R2                   0.307    McFadden's Adj R2:                 0.273 
-#> ML (Cox-Snell) R2:              0.299    Cragg-Uhler(Nagelkerke) R2:        0.436 
-#> McKelvey & Zavoina's R2:        0.518    Efron's R2:                        0.330 
-#> Count R2:                       0.810    Adj Count R2:                      0.283 
-#> BIC:                          181.430    AIC:                             168.236 
-#> ---------------------------------------------------------------------------------
    +blr_model_fit_stats(model) +#> Model Fit Statistics +#> --------------------------------------------------------------------------------- +#> Log-Lik Intercept Only: -115.644 Log-Lik Full Model: -80.118 +#> Deviance(196): 160.236 LR(3): 71.052 +#> Prob > LR: 0.000 +#> MCFadden's R2 0.307 McFadden's Adj R2: 0.273 +#> ML (Cox-Snell) R2: 0.299 Cragg-Uhler(Nagelkerke) R2: 0.436 +#> McKelvey & Zavoina's R2: 0.518 Efron's R2: 0.330 +#> Count R2: 0.810 Adj Count R2: 0.283 +#> BIC: 181.430 AIC: 168.236 +#> ---------------------------------------------------------------------------------
    -
    -

    -Confusion Matrix

    +
    +

    Confusion Matrix +

    -blr_confusion_matrix(model)
-#> Confusion Matrix and Statistics 
-#> 
-#>           Reference
-#> Prediction   0   1
-#>          0 135  26
-#>          1  12  27
-#> 
-#> 
-#>                 Accuracy : 0.8100 
-#>      No Information Rate : 0.7350 
-#> 
-#>                    Kappa : 0.4673 
-#> 
-#> McNemars's Test P-Value  : 0.0350 
-#> 
-#>              Sensitivity : 0.5094 
-#>              Specificity : 0.9184 
-#>           Pos Pred Value : 0.6923 
-#>           Neg Pred Value : 0.8385 
-#>               Prevalence : 0.2650 
-#>           Detection Rate : 0.1350 
-#>     Detection Prevalence : 0.1950 
-#>        Balanced Accuracy : 0.7139 
-#>                Precision : 0.6923 
-#>                   Recall : 0.5094 
-#> 
-#>         'Positive' Class : 1
    +blr_confusion_matrix(model) +#> Confusion Matrix and Statistics +#> +#> Reference +#> Prediction 0 1 +#> 0 135 26 +#> 1 12 27 +#> +#> +#> Accuracy : 0.8100 +#> No Information Rate : 0.7350 +#> +#> Kappa : 0.4673 +#> +#> McNemars's Test P-Value : 0.0350 +#> +#> Sensitivity : 0.5094 +#> Specificity : 0.9184 +#> Pos Pred Value : 0.6923 +#> Neg Pred Value : 0.8385 +#> Prevalence : 0.2650 +#> Detection Rate : 0.1350 +#> Detection Prevalence : 0.1950 +#> Balanced Accuracy : 0.7139 +#> Precision : 0.6923 +#> Recall : 0.5094 +#> +#> 'Positive' Class : 1
    -
    -

    -Hosmer Lemeshow Test

    +
    +

    Hosmer Lemeshow Test +

    -blr_test_hosmer_lemeshow(model)
-#>            Partition for the Hosmer & Lemeshow Test            
-#> --------------------------------------------------------------
-#>                         def = 1                 def = 0        
-#> Group    Total    Observed    Expected    Observed    Expected 
-#> --------------------------------------------------------------
-#>   1       20         0          0.16         20        19.84   
-#>   2       20         0          0.53         20        19.47   
-#>   3       20         2          0.99         18        19.01   
-#>   4       20         1          1.64         19        18.36   
-#>   5       21         3          2.72         18        18.28   
-#>   6       19         3          4.05         16        14.95   
-#>   7       20         7          6.50         13        13.50   
-#>   8       20         10         8.90         10        11.10   
-#>   9       20         13        11.49         7          8.51   
-#>  10       20         14        16.02         6          3.98   
-#> --------------------------------------------------------------
-#> 
-#>      Goodness of Fit Test      
-#> ------------------------------
-#> Chi-Square    DF    Pr > ChiSq 
-#> ------------------------------
-#>   4.4998      8       0.8095   
-#> ------------------------------
    +blr_test_hosmer_lemeshow(model) +#> Partition for the Hosmer & Lemeshow Test +#> -------------------------------------------------------------- +#> def = 1 def = 0 +#> Group Total Observed Expected Observed Expected +#> -------------------------------------------------------------- +#> 1 20 0 0.16 20 19.84 +#> 2 20 0 0.53 20 19.47 +#> 3 20 2 0.99 18 19.01 +#> 4 20 1 1.64 19 18.36 +#> 5 21 3 2.72 18 18.28 +#> 6 19 3 4.05 16 14.95 +#> 7 20 7 6.50 13 13.50 +#> 8 20 10 8.90 10 11.10 +#> 9 20 13 11.49 7 8.51 +#> 10 20 14 16.02 6 3.98 +#> -------------------------------------------------------------- +#> +#> Goodness of Fit Test +#> ------------------------------ +#> Chi-Square DF Pr > ChiSq +#> ------------------------------ +#> 4.4998 8 0.8095 +#> ------------------------------
    -
    -

    -Gains Table

    +
    +

    Gains Table +

    -blr_gains_table(model)
-#>    decile total  1  0       ks tp  tn  fp fn sensitivity specificity accuracy
-#> 1       1    20 14  6 22.33346 14 141   6 39    26.41509    95.91837     77.5
-#> 2       2    20 13  7 42.09986 27 134  13 26    50.94340    91.15646     80.5
-#> 3       3    20 10 10 54.16506 37 124  23 16    69.81132    84.35374     80.5
-#> 4       4    20  7 13 58.52907 44 111  36  9    83.01887    75.51020     77.5
-#> 5       5    20  3 17 52.62482 47  94  53  6    88.67925    63.94558     70.5
-#> 6       6    20  3 17 46.72058 50  77  70  3    94.33962    52.38095     63.5
-#> 7       7    20  1 19 35.68220 51  58  89  2    96.22642    39.45578     54.5
-#> 8       8    20  2 18 27.21088 53  40 107  0   100.00000    27.21088     46.5
-#> 9       9    20  0 20 13.60544 53  20 127  0   100.00000    13.60544     36.5
-#> 10     10    20  0 20  0.00000 53   0 147  0   100.00000     0.00000     26.5
    +blr_gains_table(model) +#> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy +#> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 +#> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 +#> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 +#> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 +#> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 +#> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 +#> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 +#> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 +#> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 +#> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5
    -
    -

    -Lift Chart

    +
    +

    Lift Chart +

    -model %>%
-  blr_gains_table() %>%
-  plot()
    +model %>% + blr_gains_table() %>% + plot()

    -
    -

    -ROC Curve

    +
    +

    ROC Curve +

    +model %>% + blr_gains_table() %>% + blr_roc_curve()

    -
    -

    -KS Chart

    +
    +

    KS Chart +

    +model %>% + blr_gains_table() %>% + blr_ks_chart()

    -
    -

    -Lorenz Curve

    +
    +

    Lorenz Curve +

    +blr_lorenz_curve(model)

    -
    -

    -Getting Help

    -

    If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.

    +
    +

    Getting Help +

    +

    If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.

    -
    -

    -Code of Conduct

    -

    Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

    +
    +

    Code of Conduct +

    +

    Please note that the blorr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

    -
    - -
    +
    +

    Dev status

    +
      +
    • CRAN_Status_Badge
      • +
    • R-CMD-check
      • +
    • Coverage status
      • +
    +
    + +
    -
    - + + diff --git a/docs/katex-auto.js b/docs/katex-auto.js new file mode 100644 index 00000000..20651d9f --- /dev/null +++ b/docs/katex-auto.js @@ -0,0 +1,14 @@ +// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345 +document.addEventListener("DOMContentLoaded", function () { + var mathElements = document.getElementsByClassName("math"); + var macros = []; + for (var i = 0; i < mathElements.length; i++) { + var texText = mathElements[i].firstChild; + if (mathElements[i].tagName == "SPAN") { + katex.render(texText.data, mathElements[i], { + displayMode: mathElements[i].classList.contains("display"), + throwOnError: false, + macros: macros, + fleqn: false + }); + }}}); diff --git a/docs/lightswitch.js b/docs/lightswitch.js new file mode 100644 index 00000000..9467125a --- /dev/null +++ b/docs/lightswitch.js @@ -0,0 +1,85 @@ + +/*! + * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under the Creative Commons Attribution 3.0 Unported License. + * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0. + */ + +const getStoredTheme = () => localStorage.getItem('theme') +const setStoredTheme = theme => localStorage.setItem('theme', theme) + +const getPreferredTheme = () => { + const storedTheme = getStoredTheme() + if (storedTheme) { + return storedTheme + } + + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light' +} + +const setTheme = theme => { + if (theme === 'auto') { + document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light')) + } else { + document.documentElement.setAttribute('data-bs-theme', theme) + } +} + +function bsSetupThemeToggle () { + 'use strict' + + const showActiveTheme = (theme, focus = false) => { + var activeLabel, activeIcon; + + document.querySelectorAll('[data-bs-theme-value]').forEach(element => { + const buttonTheme = element.getAttribute('data-bs-theme-value') + const isActive = buttonTheme == theme + + element.classList.toggle('active', isActive) + element.setAttribute('aria-pressed', isActive) + + if (isActive) { + activeLabel = element.textContent; + activeIcon = element.querySelector('span').classList.value; + } + }) + + const themeSwitcher = document.querySelector('#dropdown-lightswitch') + if (!themeSwitcher) { + return + } + + themeSwitcher.setAttribute('aria-label', activeLabel) + themeSwitcher.querySelector('span').classList.value = activeIcon; + + if (focus) { + themeSwitcher.focus() + } + } + + window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => { + const storedTheme = getStoredTheme() + if (storedTheme !== 'light' && storedTheme !== 'dark') { + setTheme(getPreferredTheme()) + } + }) + + window.addEventListener('DOMContentLoaded', () => { + showActiveTheme(getPreferredTheme()) + + document + .querySelectorAll('[data-bs-theme-value]') + .forEach(toggle => { + toggle.addEventListener('click', () => { + const theme = toggle.getAttribute('data-bs-theme-value') + setTheme(theme) + setStoredTheme(theme) + showActiveTheme(theme, true) + }) + }) + }) +} + +setTheme(getPreferredTheme()); +bsSetupThemeToggle(); diff --git a/docs/news/index.html b/docs/news/index.html index bc0e5235..c1bdb1d5 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -1,210 +1,97 @@ - - - - - - +Changelog • blorr + Skip to contents -Changelog • blorr +
    -
    -
    -

    Changelog

    - Source: NEWS.md +
    + +

    Changelog

    + Source: NEWS.md
    -
    -

    -blorr 0.3.0 2020-05-28 -

    +
    +

    blorr 0.3.1.9000

    +
    +
    +

    blorr 0.3.1

    +

    This is a patch release to fix minor bugs.

    +
    +
    +

    blorr 0.3.0

    CRAN release: 2020-05-28

    This is a minor release to reduce package dependencies and fix bugs.

    -
    -

    -blorr 0.2.2 2020-02-03 -

    +
    +

    blorr 0.2.2

    CRAN release: 2020-02-03

    This is a patch release to fix CRAN errors.

    -
    -

    -blorr 0.2.1 2019-03-12 -

    +
    +

    blorr 0.2.1

    CRAN release: 2019-03-12

    This is a patch release to fix bugs in the app.

    -
    -

    -blorr 0.2.0 2018-12-14 -

    +
    +

    blorr 0.2.0

    CRAN release: 2018-12-14

    This is a minor release to fix bugs and for other enhancements.

    -
    -

    -Enhancements

    -
      -
    • variable selection procedures now return the final model as an object of class glm (#58)
      • -
    • data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#64)
      • -
    • informs users about new versions (#1)
      • -
    +
    +

    Enhancements

    +
    • variable selection procedures now return the final model as an object of class glm (#58)
      • +
    • data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#64)
      • +
    • informs users about new versions (#1)
      • +
    +
    +

    Bug Fixes

    +
    • tibble package breaking changes (#74)
      • +
    -
    -

    -Bug Fixes

    -
      -
    • tibble package breaking changes (#74)
      • -
    -
    -
    -
    -

    -blorr 0.1.0 2018-05-11 -

    +
    +

    blorr 0.1.0

    CRAN release: 2018-05-11

    Initial release

    -
    +
    - +
    - -
    - - - + diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 7e7048fa..9757bf9e 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -2,85 +2,45 @@ (function($) { $(function() { - $('.navbar-fixed-top').headroom(); + $('nav.navbar').headroom(); - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") }); - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); } - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); - return(haystack.length); - } + $('[data-bs-toggle="tooltip"]').tooltip(); /* Clipboard --------------------------*/ function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); + var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); } if(ClipboardJS.isSupported()) { $(document).ready(function() { - var copyButton = ""; + var copyButton = ""; - $(".examples, div.sourceCode").addClass("hasCopyButton"); + $("div.sourceCode").addClass("hasCopyButton"); // Insert copy buttons: $(copyButton).prependTo(".hasCopyButton"); @@ -89,20 +49,106 @@ $('.btn-copy-ex').tooltip({container: 'body'}); // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + var clipboard = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { - return trigger.parentNode.textContent; + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); - clipboardBtnCopies.on('success', function(e) { + clipboard.on('success', function(e) { changeTooltipMessage(e.trigger, 'Copied!'); e.clearSelection(); }); - clipboardBtnCopies.on('error', function() { + clipboard.on('error', function(e) { changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); }); + }); } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
    ${s.title}
    `; + } else if (s.previous_headings == "") { + return `${s.dir} >
    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); })(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 2d9fcd61..665f93a1 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,10 +1,9 @@ -pandoc: 2.11.4 -pkgdown: 1.6.1 +pandoc: '3.2' +pkgdown: 2.1.0 pkgdown_sha: ~ articles: introduction: introduction.html -last_built: 2021-07-08T10:11Z +last_built: 2024-11-11T14:59Z urls: reference: https://blorr.rsquaredacademy.com/reference article: https://blorr.rsquaredacademy.com/articles - diff --git a/docs/reference/bank_marketing.html b/docs/reference/bank_marketing.html index f5238e0e..00c9338b 100644 --- a/docs/reference/bank_marketing.html +++ b/docs/reference/bank_marketing.html @@ -1,203 +1,136 @@ - - - - - - +Bank marketing data set — bank_marketing • blorr + Skip to contents -Bank marketing data set — bank_marketing • blorr +
    +
    +
    - - - +

    Bank marketing data set

    + Source: R/blr-data-bank-marketing.R +
    bank_marketing.Rd
    +
    +
    +

    The data is related with direct marketing campaigns of a Portuguese banking institution. +The marketing campaigns were based on phone calls. Often, more than one contact to the +same client was required, in order to access if the product (bank term deposit) would +be ('yes') or not ('no') subscribed.

    +
    +
    +

    Usage

    + 
    bank_marketing
    +
    +
    +

    Format

    +

    A tibble with 4521 rows and 17 variables:

    age
    +

    age of the client

    - - +
    job
    +

    type of job

    +
    marital
    +

    marital status

    +
    education
    +

    education level of the client

    +
    default
    +

    has credit in default?

    - - - +
    housing
    +

    has housing loan?

    - +
    loan
    +

    has personal loan?

    +
    contact
    +

    contact communication type

    +
    month
    +

    last contact month of year

    - +
    day_of_week
    +

    last contact day of the week

    - -
    -
    - - - - -
    +
    campaign
    +

    number of contacts performed during this campaign and for this client

    -
    -
    -
    -

    Bank marketing data set

    - Source: R/blr-data-bank-marketing.R - -
    - -
    -

    The data is related with direct marketing campaigns of a Portuguese banking institution. -The marketing campaigns were based on phone calls. Often, more than one contact to the -same client was required, in order to access if the product (bank term deposit) would -be ('yes') or not ('no') subscribed.

    -
    - - 
    bank_marketing
    +
    pdays
    +

    number of days that passed by after the client was last contacted from a previous campaign

    +
    previous
    +

    number of contacts performed before this campaign and for this clien

    -

    Format

    +
    poutcome
    +

    outcome of the previous marketing campaign

    -

    A tibble with 4521 rows and 17 variables:

    -
    age

    age of the client

    -
    job

    type of job

    -
    marital

    marital status

    -
    education

    education level of the client

    -
    default

    has credit in default?

    -
    housing

    has housing loan?

    -
    loan

    has personal loan?

    -
    contact

    contact communication type

    -
    month

    last contact month of year

    -
    day_of_week

    last contact day of the week

    -
    duration

    last contact duration, in seconds

    -
    campaign

    number of contacts performed during this campaign and for this client

    -
    pdays

    number of days that passed by after the client was last contacted from a previous campaign

    -
    previous

    number of contacts performed before this campaign and for this clien

    -
    poutcome

    outcome of the previous marketing campaign

    -
    y

    has the client subscribed a term deposit?

    +
    y
    +

    has the client subscribed a term deposit?

    -
    - -

    Source

    +
    +
    +

    Source

    [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

    +
    -
    - -
    +
    • -
    + - - - + diff --git a/docs/reference/blorr-package.html b/docs/reference/blorr-package.html new file mode 100644 index 00000000..28f8eaa2 --- /dev/null +++ b/docs/reference/blorr-package.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blorr.html b/docs/reference/blorr.html index 7af4db0a..fa809e64 100644 --- a/docs/reference/blorr.html +++ b/docs/reference/blorr.html @@ -1,175 +1,83 @@ - - - - - - +blorr package — blorr • blorr + Skip to contents -blorr package — blorr • blorr +
    -
    -
    -

    blorr package

    - Source: R/blr-blorr.R - +
    + +

    blorr package

    + Source: R/blr-blorr.R +
    blorr.Rd
    -
    +

    Tools for developing binary logistic regression models

    - -

    Details

    - +
    +

    Details

    See the README on -GitHub

    +GitHub

    +
    +
    +

    See also

    +

    Useful links:

    +
    +
    +

    Author

    +

    Maintainer: Aravind Hebbali hebbali.aravind@gmail.com (ORCID)

    +
    -
    - -
    +
    -
    + - - - + diff --git a/docs/reference/blr_bivariate_analysis.default.html b/docs/reference/blr_bivariate_analysis.default.html new file mode 100644 index 00000000..aa6cc7fe --- /dev/null +++ b/docs/reference/blr_bivariate_analysis.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_bivariate_analysis.html b/docs/reference/blr_bivariate_analysis.html index db9d0ed6..9e53c422 100644 --- a/docs/reference/blr_bivariate_analysis.html +++ b/docs/reference/blr_bivariate_analysis.html @@ -1,225 +1,139 @@ - - - - - - - -Bivariate analysis — blr_bivariate_analysis • blorr - - - - - +Bivariate analysis — blr_bivariate_analysis • blorr + Skip to contents - - +
    +
    +
    +

    Bivariate analysis

    + Source: R/blr-bivariate-analysis.R +
    blr_bivariate_analysis.Rd
    +
    - - +

    Information value and likelihood ratio chi square test for initial variable/predictor selection. Currently avialable for categorical - predictors only." /> - - + predictors only.

    +
    +
    +

    Usage

    + 
    blr_bivariate_analysis(data, response, ...)
+
+# Default S3 method
+blr_bivariate_analysis(data, response, ...)
    +
    - - - +
    +

    Arguments

    - +
    data
    +

    A tibble or a data.frame.

    - +
    response
    +

    Response variable; column in data.

    - -
    -
    - - - - -
    +
    ...
    +

    Predictor variables; columns in data.

    -
    -
    -
    -

    Bivariate analysis

    - Source: R/blr-bivariate-analysis.R - +
    +
    +

    Value

    +

    A tibble with the following columns:

    +
    Variable
    +

    Variable name

    + +
    Information Value
    +

    Information value

    + +
    LR Chi Square
    +

    Likelihood ratio statisitc

    + +
    LR DF
    +

    Likelihood ratio degrees of freedom

    + +
    LR p-value
    +

    Likelihood ratio p value

    + +
    +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_segment(), +blr_segment_dist(), +blr_segment_twoway(), +blr_woe_iv(), +blr_woe_iv_stats()

    -
    -

    Information value and likelihood ratio chi square test for initial - variable/predictor selection. Currently avialable for categorical - predictors only.

    +
    +

    Examples

    + 
    blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp)
+#>                          Bivariate Analysis                           
+#> ---------------------------------------------------------------------
+#> Variable    Information Value    LR Chi Square    LR DF    LR p-value 
+#> ---------------------------------------------------------------------
+#>  female           0.10              3.9350          1        0.0473   
+#>   prog            0.43              16.1450         2        3e-04    
+#>   race            0.33              11.3694         3        0.0099   
+#>  schtyp           0.00              0.0445          1        0.8330   
+#> ---------------------------------------------------------------------
+
+
    +
    - 
    blr_bivariate_analysis(data, response, ...)
-
-# S3 method for default
-blr_bivariate_analysis(data, response, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    A tibble or a data.frame.

    response

    Response variable; column in data.

    ...

    Predictor variables; columns in data.

    - -

    Value

    -

    A tibble with the following columns:

    -
    Variable

    Variable name

    -
    Information Value

    Information value

    -
    LR Chi Square

    Likelihood ratio statisitc

    -
    LR DF

    Likelihood ratio degrees of freedom

    -
    LR p-value

    Likelihood ratio p value

    - -

    See also

    - -

    Other bivariate analysis procedures: -blr_segment_dist(), -blr_segment_twoway(), -blr_segment(), -blr_woe_iv_stats(), -blr_woe_iv()

    - -

    Examples

    - 
    blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp)
-#>                          Bivariate Analysis                           
-#> ---------------------------------------------------------------------
-#> Variable    Information Value    LR Chi Square    LR DF    LR p-value 
-#> ---------------------------------------------------------------------
-#>  female           0.10              3.9350          1        0.0473   
-#>   prog            0.43              16.1450         2        3e-04    
-#>   race            0.33              11.3694         3        0.0099   
-#>  schtyp           0.00              0.0445          1        0.8330   
-#> ---------------------------------------------------------------------
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_coll_diag.html b/docs/reference/blr_coll_diag.html index 8f200a1a..61613320 100644 --- a/docs/reference/blr_coll_diag.html +++ b/docs/reference/blr_coll_diag.html @@ -1,168 +1,79 @@ - - - - - - +Collinearity diagnostics — blr_coll_diag • blorr + Skip to contents -Collinearity diagnostics — blr_coll_diag • blorr +
    -
    -
    -

    Collinearity diagnostics

    - Source: R/blr-collinearity-diagnostics.R - +
    + +

    Collinearity diagnostics

    + Source: R/blr-collinearity-diagnostics.R +
    blr_coll_diag.Rd
    -
    +

    Variance inflation factor, tolerance, eigenvalues and condition indices.

    - 
    blr_coll_diag(model)
-
-blr_vif_tol(model)
+    
    
+    
    Usage
    
+    
    blr_coll_diag(model)
+
+blr_vif_tol(model)
+
+blr_eigen_cindex(model)
    
+    
    
 
-blr_eigen_cindex(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    blr_coll_diag returns an object of class "blr_coll_diag". An object of class "blr_coll_diag" is a list containing the following components:

    -
    vif_t

    tolerance and variance inflation factors

    -
    eig_cindex

    eigen values and condition index

    +
    vif_t
    +

    tolerance and variance inflation factors

    -

    Details

    +
    eig_cindex
    +

    eigen values and condition index

    +
    +
    +

    Details

    Collinearity implies two variables are near perfect linear combinations of one another. Multicollinearity involves more than two variables. In the presence of multicollinearity, regression estimates are unstable and have @@ -186,85 +97,81 @@

    Details of each of these linear combinations is called an eigenvalue. Collinearity is spotted by finding 2 or more variables that have large proportions of variance (.50 or more) that correspond to large condition indices. A rule of thumb is to label as large those condition indices in the range of 30 or larger.

    -

    References

    - +
    +
    +

    References

    Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons.

    +
    -

    Examples

    - 
    # model
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-# vif and tolerance
-blr_vif_tol(model)
-#>   Variable Tolerance      VIF
-#> 1  female1 0.9824347 1.017879
-#> 2     read 0.6021370 1.660752
-#> 3  science 0.5939847 1.683545
-
-# eigenvalues and condition indices
-blr_eigen_cindex(model)
-#>   Eigenvalue Condition Index    intercept      female1         read
-#> 1 3.57391760        1.000000 3.500782e-06 2.232611e-05 0.0006577134
-#> 2 0.39409893        3.011408 5.155657e-06 8.559696e-04 0.0015266309
-#> 3 0.01888407       13.757025 1.632498e-03 4.397949e-05 0.1143698337
-#> 4 0.01309940       16.517583 5.619292e-05 1.312420e-05 0.2758987745
-#>        science
-#> 1 0.0009448623
-#> 2 0.0025603607
-#> 3 0.0545537252
-#> 4 0.5468553525
-
-# collinearity diagnostics
-blr_coll_diag(model)
-#> Tolerance and Variance Inflation Factor
-#> ---------------------------------------
-#>   Variable Tolerance      VIF
-#> 1  female1 0.9824347 1.017879
-#> 2     read 0.6021370 1.660752
-#> 3  science 0.5939847 1.683545
-#> 
-#> 
-#> Eigenvalue and Condition Index
-#> ------------------------------
-#>   Eigenvalue Condition Index    intercept      female1         read
-#> 1 3.57391760        1.000000 3.500782e-06 2.232611e-05 0.0006577134
-#> 2 0.39409893        3.011408 5.155657e-06 8.559696e-04 0.0015266309
-#> 3 0.01888407       13.757025 1.632498e-03 4.397949e-05 0.1143698337
-#> 4 0.01309940       16.517583 5.619292e-05 1.312420e-05 0.2758987745
-#>        science
-#> 1 0.0009448623
-#> 2 0.0025603607
-#> 3 0.0545537252
-#> 4 0.5468553525
-
-
    -
    - -
    +
    +

    Examples

    + 
    # model
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+# vif and tolerance
+blr_vif_tol(model)
+#>   Variable Tolerance      VIF
+#> 1  female1 0.9824347 1.017879
+#> 2     read 0.6021370 1.660752
+#> 3  science 0.5939847 1.683545
+
+# eigenvalues and condition indices
+blr_eigen_cindex(model)
+#>   Eigenvalue Condition Index    intercept      female1         read
+#> 1 3.57391760        1.000000 3.500782e-06 2.232611e-05 0.0006577134
+#> 2 0.39409893        3.011408 5.155657e-06 8.559696e-04 0.0015266309
+#> 3 0.01888407       13.757025 1.632498e-03 4.397949e-05 0.1143698337
+#> 4 0.01309940       16.517583 5.619292e-05 1.312420e-05 0.2758987745
+#>        science
+#> 1 0.0009448623
+#> 2 0.0025603607
+#> 3 0.0545537252
+#> 4 0.5468553525
+
+# collinearity diagnostics
+blr_coll_diag(model)
+#> Tolerance and Variance Inflation Factor
+#> ---------------------------------------
+#>   Variable Tolerance      VIF
+#> 1  female1 0.9824347 1.017879
+#> 2     read 0.6021370 1.660752
+#> 3  science 0.5939847 1.683545
+#> 
+#> 
+#> Eigenvalue and Condition Index
+#> ------------------------------
+#>   Eigenvalue Condition Index    intercept      female1         read
+#> 1 3.57391760        1.000000 3.500782e-06 2.232611e-05 0.0006577134
+#> 2 0.39409893        3.011408 5.155657e-06 8.559696e-04 0.0015266309
+#> 3 0.01888407       13.757025 1.632498e-03 4.397949e-05 0.1143698337
+#> 4 0.01309940       16.517583 5.619292e-05 1.312420e-05 0.2758987745
+#>        science
+#> 1 0.0009448623
+#> 2 0.0025603607
+#> 3 0.0545537252
+#> 4 0.5468553525
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_confusion_matrix.default.html b/docs/reference/blr_confusion_matrix.default.html new file mode 100644 index 00000000..4d26c07d --- /dev/null +++ b/docs/reference/blr_confusion_matrix.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_confusion_matrix.html b/docs/reference/blr_confusion_matrix.html index 627730f6..4679efef 100644 --- a/docs/reference/blr_confusion_matrix.html +++ b/docs/reference/blr_confusion_matrix.html @@ -1,243 +1,146 @@ - - - - - - +Confusion matrix — blr_confusion_matrix • blorr + Skip to contents -Confusion matrix — blr_confusion_matrix • blorr +
    +
    +
    +

    Confusion matrix

    + Source: R/blr-model-validation.R +
    blr_confusion_matrix.Rd
    +
    +
    +

    Confusion matrix and statistics.

    +
    +
    +

    Usage

    + 
    blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)
+
+# Default S3 method
+blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)
    +
    - - +
    +

    Arguments

    +
    model
    +

    An object of class glm.

    - - - +
    cutoff
    +

    Cutoff for classification.

    - +
    data
    +

    A tibble or a data.frame.

    - +
    ...
    +

    Other arguments.

    - -
    -
    -
    +
    +

    Value

    +

    Confusion matix.

    - - -
    -
    - - - - - -
    -
    -
    -

    Confusion matrix

    - Source: R/blr-model-validation.R - +
    +

    See also

    +

    Other model validation techniques: +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    -
    -

    Confusion matrix and statistics.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_confusion_matrix(model, cutoff = 0.4)
+#> Confusion Matrix and Statistics 
+#> 
+#>           Reference
+#> Prediction   0   1
+#>          0 125  16
+#>          1  22  37
+#> 
+#> 
+#>                 Accuracy : 0.8100 
+#>      No Information Rate : 0.7350 
+#> 
+#>                    Kappa : 0.5293 
+#> 
+#> McNemars's Test P-Value  : 0.4173 
+#> 
+#>              Sensitivity : 0.6981 
+#>              Specificity : 0.8503 
+#>           Pos Pred Value : 0.6271 
+#>           Neg Pred Value : 0.8865 
+#>               Prevalence : 0.2650 
+#>           Detection Rate : 0.1850 
+#>     Detection Prevalence : 0.2950 
+#>        Balanced Accuracy : 0.7742 
+#>                Precision : 0.6271 
+#>                   Recall : 0.6981 
+#> 
+#>         'Positive' Class : 1
+
+
    +
    - 
    blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)
-
-# S3 method for default
-blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    cutoff

    Cutoff for classification.

    data

    A tibble or a data.frame.

    ...

    Other arguments.

    - -

    Value

    -

    Confusion matix.

    -

    See also

    - -

    Other model validation techniques: -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_confusion_matrix(model, cutoff = 0.4)
-#> Confusion Matrix and Statistics 
-#> 
-#>           Reference
-#> Prediction   0   1
-#>          0 125  16
-#>          1  22  37
-#> 
-#> 
-#>                 Accuracy : 0.8100 
-#>      No Information Rate : 0.7350 
-#> 
-#>                    Kappa : 0.5293 
-#> 
-#> McNemars's Test P-Value  : 0.4173 
-#> 
-#>              Sensitivity : 0.6981 
-#>              Specificity : 0.8503 
-#>           Pos Pred Value : 0.6271 
-#>           Neg Pred Value : 0.8865 
-#>               Prevalence : 0.2650 
-#>           Detection Rate : 0.1850 
-#>     Detection Prevalence : 0.2950 
-#>        Balanced Accuracy : 0.7742 
-#>                Precision : 0.6271 
-#>                   Recall : 0.6981 
-#> 
-#>         'Positive' Class : 1
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_decile_capture_rate-1.png b/docs/reference/blr_decile_capture_rate-1.png index 15af711a..a65f4429 100644 Binary files a/docs/reference/blr_decile_capture_rate-1.png and b/docs/reference/blr_decile_capture_rate-1.png differ diff --git a/docs/reference/blr_decile_capture_rate.html b/docs/reference/blr_decile_capture_rate.html index 8b76da44..1fb0f483 100644 --- a/docs/reference/blr_decile_capture_rate.html +++ b/docs/reference/blr_decile_capture_rate.html @@ -1,236 +1,138 @@ - - - - - - +Event rate by decile — blr_decile_capture_rate • blorr + Skip to contents -Event rate by decile — blr_decile_capture_rate • blorr +
    +
    +
    - - - +

    Event rate by decile

    + Source: R/blr-gains-table.R +
    blr_decile_capture_rate.Rd
    +
    +
    +

    Visualize the decile wise event rate.

    +
    +
    +

    Usage

    + 
    blr_decile_capture_rate(
+  gains_table,
+  xaxis_title = "Decile",
+  yaxis_title = "Capture Rate",
+  title = "Capture Rate by Decile",
+  bar_color = "blue",
+  text_size = 3.5,
+  text_vjust = -0.3,
+  print_plot = TRUE
+)
    +
    +
    +

    Arguments

    - - +
    gains_table
    +

    An object of class blr_gains_table.

    +
    xaxis_title
    +

    X axis title.

    - - - - +
    yaxis_title
    +

    Y axis title.

    +
    title
    +

    Plot title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Event rate by decile

    - Source: R/blr-gains-table.R - -
    +
    text_size
    +

    Size of the bar labels.

    -
    -

    Visualize the decile wise event rate.

    + +
    text_vjust
    +

    Vertical justification of the bar labels.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    - 
    blr_decile_capture_rate(
-  gains_table,
-  xaxis_title = "Decile",
-  yaxis_title = "Capture Rate",
-  title = "Capture Rate by Decile",
-  bar_color = "blue",
-  text_size = 3.5,
-  text_vjust = -0.3,
-  print_plot = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    gains_table

    An object of class blr_gains_table.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    title

    Plot title.

    bar_color

    Bar color.

    text_size

    Size of the bar labels.

    text_vjust

    Vertical justification of the bar labels.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_decile_capture_rate(gt)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_decile_capture_rate(gt)
+
+
+
    +
    +
    - + - - - + diff --git a/docs/reference/blr_decile_lift_chart-1.png b/docs/reference/blr_decile_lift_chart-1.png index 964b77af..3e66e720 100644 Binary files a/docs/reference/blr_decile_lift_chart-1.png and b/docs/reference/blr_decile_lift_chart-1.png differ diff --git a/docs/reference/blr_decile_lift_chart.html b/docs/reference/blr_decile_lift_chart.html index 8520d489..c39b13f8 100644 --- a/docs/reference/blr_decile_lift_chart.html +++ b/docs/reference/blr_decile_lift_chart.html @@ -1,236 +1,138 @@ - - - - - - +Decile lift chart — blr_decile_lift_chart • blorr + Skip to contents -Decile lift chart — blr_decile_lift_chart • blorr +
    +
    +
    - - - +

    Decile lift chart

    + Source: R/blr-gains-table.R +
    blr_decile_lift_chart.Rd
    +
    +
    +

    Decile wise lift chart.

    +
    +
    +

    Usage

    + 
    blr_decile_lift_chart(
+  gains_table,
+  xaxis_title = "Decile",
+  yaxis_title = "Decile Mean / Global Mean",
+  title = "Decile Lift Chart",
+  bar_color = "blue",
+  text_size = 3.5,
+  text_vjust = -0.3,
+  print_plot = TRUE
+)
    +
    +
    +

    Arguments

    - - +
    gains_table
    +

    An object of class blr_gains_table.

    +
    xaxis_title
    +

    X axis title.

    - - - - +
    yaxis_title
    +

    Y axis title.

    +
    title
    +

    Plot title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Decile lift chart

    - Source: R/blr-gains-table.R - -
    +
    text_size
    +

    Size of the bar labels.

    -
    -

    Decile wise lift chart.

    + +
    text_vjust
    +

    Vertical justification of the bar labels.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    - 
    blr_decile_lift_chart(
-  gains_table,
-  xaxis_title = "Decile",
-  yaxis_title = "Decile Mean / Global Mean",
-  title = "Decile Lift Chart",
-  bar_color = "blue",
-  text_size = 3.5,
-  text_vjust = -0.3,
-  print_plot = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    gains_table

    An object of class blr_gains_table.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    title

    Plot title.

    bar_color

    Color of the bars.

    text_size

    Size of the bar labels.

    text_vjust

    Vertical justification of the bar labels.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_decile_lift_chart(gt)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_decile_lift_chart(gt)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_eigen_cindex.html b/docs/reference/blr_eigen_cindex.html new file mode 100644 index 00000000..2ae717b1 --- /dev/null +++ b/docs/reference/blr_eigen_cindex.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_gains_table-1.png b/docs/reference/blr_gains_table-1.png index e5af26e2..ec54ed27 100644 Binary files a/docs/reference/blr_gains_table-1.png and b/docs/reference/blr_gains_table-1.png differ diff --git a/docs/reference/blr_gains_table.html b/docs/reference/blr_gains_table.html index 9d539389..61cd83e3 100644 --- a/docs/reference/blr_gains_table.html +++ b/docs/reference/blr_gains_table.html @@ -1,280 +1,185 @@ - - - - - - +Gains table & lift chart — blr_gains_table • blorr + Skip to contents -Gains table & lift chart — blr_gains_table • blorr +
    +
    +
    - - - +

    Gains table & lift chart

    + Source: R/blr-gains-table.R +
    blr_gains_table.Rd
    +
    +
    +

    Compute sensitivity, specificity, accuracy and KS statistics to + generate the lift chart and the KS chart.

    +
    +
    +

    Usage

    + 
    blr_gains_table(model, data = NULL)
+
+# S3 method for class 'blr_gains_table'
+plot(
+  x,
+  title = "Lift Chart",
+  xaxis_title = "% Population",
+  yaxis_title = "% Cumulative 1s",
+  diag_line_col = "red",
+  lift_curve_col = "blue",
+  plot_title_justify = 0.5,
+  print_plot = TRUE,
+  ...
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    data
    +

    A tibble or a data.frame.

    - - - - +
    x
    +

    An object of class blr_gains_table.

    +
    title
    +

    Plot title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Gains table & lift chart

    - Source: R/blr-gains-table.R - -
    +
    yaxis_title
    +

    Y axis title.

    -
    -

    Compute sensitivity, specificity, accuracy and KS statistics to - generate the lift chart and the KS chart.

    -
    - 
    blr_gains_table(model, data = NULL)
-
-# S3 method for blr_gains_table
-plot(
-  x,
-  title = "Lift Chart",
-  xaxis_title = "% Population",
-  yaxis_title = "% Cumulative 1s",
-  diag_line_col = "red",
-  lift_curve_col = "blue",
-  plot_title_justify = 0.5,
-  print_plot = TRUE,
-  ...
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    data

    A tibble or a data.frame.

    x

    An object of class blr_gains_table.

    title

    Plot title.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    diag_line_col

    Diagonal line color.

    lift_curve_col

    Color of the lift curve.

    plot_title_justify

    Horizontal justification on the plot title.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    ...

    Other inputs.

    - -

    Value

    +
    diag_line_col
    +

    Diagonal line color.

    + + +
    lift_curve_col
    +

    Color of the lift curve.

    -

    A tibble.

    -

    References

    +
    plot_title_justify
    +

    Horizontal justification on the plot title.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + + +
    ...
    +

    Other inputs.

    + +
    +
    +

    Value

    +

    A tibble.

    +
    +
    +

    References

    Agresti, A. (2007), An Introduction to Categorical Data Analysis, Second Edition, New York: John Wiley & Sons.

    Agresti, A. (2013), Categorical Data Analysis, Third Edition, New York: John Wiley & Sons.

    Thomas LC (2009): Consumer Credit Models: Pricing, Profit, and Portfolio. Oxford, Oxford Uni-versity Press.

    Sobehart J, Keenan S, Stein R (2000): Benchmarking Quantitative Default Risk Models: A Validation Methodology, Moody’s Investors Service.

    -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-# gains table
-blr_gains_table(model)
-#>    decile total  1  0       ks tp  tn  fp fn sensitivity specificity accuracy
-#> 1       1    20 14  6 22.33346 14 141   6 39    26.41509    95.91837     77.5
-#> 2       2    20 13  7 42.09986 27 134  13 26    50.94340    91.15646     80.5
-#> 3       3    20 10 10 54.16506 37 124  23 16    69.81132    84.35374     80.5
-#> 4       4    20  7 13 58.52907 44 111  36  9    83.01887    75.51020     77.5
-#> 5       5    20  3 17 52.62482 47  94  53  6    88.67925    63.94558     70.5
-#> 6       6    20  3 17 46.72058 50  77  70  3    94.33962    52.38095     63.5
-#> 7       7    20  1 19 35.68220 51  58  89  2    96.22642    39.45578     54.5
-#> 8       8    20  2 18 27.21088 53  40 107  0   100.00000    27.21088     46.5
-#> 9       9    20  0 20 13.60544 53  20 127  0   100.00000    13.60544     36.5
-#> 10     10    20  0 20  0.00000 53   0 147  0   100.00000     0.00000     26.5
-
-# lift chart
-k <- blr_gains_table(model)
-plot(k)
-
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+# gains table
+blr_gains_table(model)
+#>    decile total  1  0       ks tp  tn  fp fn sensitivity specificity accuracy
+#> 1       1    20 14  6 22.33346 14 141   6 39    26.41509    95.91837     77.5
+#> 2       2    20 13  7 42.09986 27 134  13 26    50.94340    91.15646     80.5
+#> 3       3    20 10 10 54.16506 37 124  23 16    69.81132    84.35374     80.5
+#> 4       4    20  7 13 58.52907 44 111  36  9    83.01887    75.51020     77.5
+#> 5       5    20  3 17 52.62482 47  94  53  6    88.67925    63.94558     70.5
+#> 6       6    20  3 17 46.72058 50  77  70  3    94.33962    52.38095     63.5
+#> 7       7    20  1 19 35.68220 51  58  89  2    96.22642    39.45578     54.5
+#> 8       8    20  2 18 27.21088 53  40 107  0   100.00000    27.21088     46.5
+#> 9       9    20  0 20 13.60544 53  20 127  0   100.00000    13.60544     36.5
+#> 10     10    20  0 20  0.00000 53   0 147  0   100.00000     0.00000     26.5
+
+# lift chart
+k <- blr_gains_table(model)
+plot(k)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_gini_index.html b/docs/reference/blr_gini_index.html index 02926c7f..b0206954 100644 --- a/docs/reference/blr_gini_index.html +++ b/docs/reference/blr_gini_index.html @@ -1,217 +1,122 @@ - - - - - - - -Gini index — blr_gini_index • blorr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Gini index — blr_gini_index • blorr - - + for measuring discriminatory power."> + Skip to contents - - - +
    -
    -
    -

    Gini index

    - Source: R/blr-lorenz-curve.R - +
    + +

    Gini index

    + Source: R/blr-lorenz-curve.R +
    blr_gini_index.Rd
    -
    +

    Gini index is a measure of inequality and was developed to measure income inequality in labour market. In the predictive model, Gini Index is used for measuring discriminatory power.

    - 
    blr_gini_index(model, data = NULL)
    +
    +

    Usage

    + 
    blr_gini_index(model, data = NULL)
    +
    -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    data

    A tibble or data.frame.

    +
    +

    Arguments

    -

    Value

    -

    Gini index.

    -

    References

    +
    model
    +

    An object of class glm.

    + +
    data
    +

    A tibble or data.frame.

    + +
    +
    +

    Value

    +

    Gini index.

    +
    +
    +

    References

    Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.

    Müller M, Rönz B (2000): Credit Scoring using Semiparametric Methods. In: Franke J, Härdle W, Stahl G (Eds.): Measuring Risk in Complex Stochastic Systems. New York, Springer-Verlag.

    -

    https://doi.org/10.2753/REE1540-496X470605

    -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_gini_index(model)
-#> [1] 0.5252134
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_gini_index(model)
+#> [1] 0.5252134
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_ks_chart-1.png b/docs/reference/blr_ks_chart-1.png index 72e09468..a99c1d9e 100644 Binary files a/docs/reference/blr_ks_chart-1.png and b/docs/reference/blr_ks_chart-1.png differ diff --git a/docs/reference/blr_ks_chart.html b/docs/reference/blr_ks_chart.html index 99b98647..59d0cbf3 100644 --- a/docs/reference/blr_ks_chart.html +++ b/docs/reference/blr_ks_chart.html @@ -1,238 +1,144 @@ - - - - - - +KS chart — blr_ks_chart • blorr + Skip to contents -KS chart — blr_ks_chart • blorr +
    +
    +
    - - - +

    KS chart

    + Source: R/blr-gains-table.R +
    blr_ks_chart.Rd
    +
    +
    +

    Kolmogorov-Smirnov (KS) statistics is used to assess predictive power for +marketing or credit risk models. It is the maximum difference between +cumulative event and non-event distribution across score/probability bands. +The gains table typically has +across score bands and can be used to find the KS for a model.

    +
    +
    +

    Usage

    + 
    blr_ks_chart(
+  gains_table,
+  title = "KS Chart",
+  yaxis_title = " ",
+  xaxis_title = "Cumulative Population %",
+  ks_line_color = "black",
+  print_plot = TRUE
+)
    +
    +
    +

    Arguments

    - - +
    gains_table
    +

    An object of class blr_gains_table.

    +
    title
    +

    Plot title.

    - - - - +
    yaxis_title
    +

    Y axis title.

    +
    xaxis_title
    +

    X axis title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    KS chart

    - Source: R/blr-gains-table.R - -
    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    -
    -

    Kolmogorov-Smirnov (KS) statistics is used to assess predictive power for -marketing or credit risk models. It is the maximum difference between -cumulative event and non-event distribution across score/probability bands. -The gains table typically has -across score bands and can be used to find the KS for a model.

    +
    +
    +

    References

    +

    https://pubmed.ncbi.nlm.nih.gov/843576/

    +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_lorenz_curve(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    - 
    blr_ks_chart(
-  gains_table,
-  title = "KS Chart",
-  yaxis_title = " ",
-  xaxis_title = "Cumulative Population %",
-  ks_line_color = "black",
-  print_plot = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    gains_table

    An object of class blr_gains_table.

    title

    Plot title.

    yaxis_title

    Y axis title.

    xaxis_title

    X axis title.

    ks_line_color

    Color of the line indicating maximum KS statistic.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    References

    - -

    https://doi.org/10.1198/tast.2009.08210

    -

    https://www.ncbi.nlm.nih.gov/pubmed/843576

    -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_lorenz_curve(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_ks_chart(gt)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_ks_chart(gt)
+
+
+
    +
    +
    - + - - - + diff --git a/docs/reference/blr_launch_app.html b/docs/reference/blr_launch_app.html index 8510bb4d..e4cc2223 100644 --- a/docs/reference/blr_launch_app.html +++ b/docs/reference/blr_launch_app.html @@ -1,177 +1,78 @@ - - - - - - +Launch shiny app — blr_launch_app • blorr + Skip to contents -Launch shiny app — blr_launch_app • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Launch shiny app

    - Source: R/blr-launch-app.R - +
    +

    Launches shiny app for interactive model building.

    -
    -

    Launches shiny app for interactive model building.

    +
    +

    Usage

    + 
    blr_launch_app()
    - 
    blr_launch_app()
    +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+blr_launch_app()
+} # }
+
    +
    +
    -

    Examples

    - 
    if (FALSE) {
-blr_launch_app()
-}
-
    -
    - + - - - - + diff --git a/docs/reference/blr_linktest.html b/docs/reference/blr_linktest.html index d477c417..03cbf9ad 100644 --- a/docs/reference/blr_linktest.html +++ b/docs/reference/blr_linktest.html @@ -1,221 +1,120 @@ - - - - - - +Model specification error — blr_linktest • blorr + Skip to contents -Model specification error — blr_linktest • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Model specification error

    - Source: R/blr-linktest.R - +
    +

    Test for model specification error.

    -
    -

    Test for model specification error.

    +
    +

    Usage

    + 
    blr_linktest(model)
    - 
    blr_linktest(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    An object of class glm.

    -

    References

    - +
    +
    +

    References

    Pregibon, D. 1979. Data analytic methods for generalized linear models. PhD diss., University of Toronto.

    Pregibon, D. 1980. Goodness of link tests for generalized linear models.

    Tukey, J. W. 1949. One degree of freedom for non-additivity.

    +
    -

    Examples

    - 
    
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_linktest(model)
-#> 
-#> Call:
-#> glm(formula = resp ~ fit + fit2, family = binomial(link = "logit"), 
-#>     data = newdat)
-#> 
-#> Deviance Residuals: 
-#>     Min       1Q   Median       3Q      Max  
-#> -1.8578  -0.6168  -0.2987   0.5102   2.6096  
-#> 
-#> Coefficients:
-#>             Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)  0.03569    0.23399   0.153    0.879    
-#> fit          0.93715    0.20767   4.513  6.4e-06 ***
-#> fit2        -0.03920    0.08970  -0.437    0.662    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for binomial family taken to be 1)
-#> 
-#>     Null deviance: 231.29  on 199  degrees of freedom
-#> Residual deviance: 160.04  on 197  degrees of freedom
-#> AIC: 166.04
-#> 
-#> Number of Fisher Scoring iterations: 6
-#> 
-
-
    -
    - -
    +
    +

    Examples

    + 
    
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_linktest(model)
+#> 
+#> Call:
+#> glm(formula = resp ~ fit + fit2, family = binomial(link = "logit"), 
+#>     data = newdat)
+#> 
+#> Coefficients:
+#>             Estimate Std. Error z value Pr(>|z|)    
+#> (Intercept)  0.03569    0.23399   0.153    0.879    
+#> fit          0.93715    0.20767   4.513  6.4e-06 ***
+#> fit2        -0.03920    0.08970  -0.437    0.662    
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> (Dispersion parameter for binomial family taken to be 1)
+#> 
+#>     Null deviance: 231.29  on 199  degrees of freedom
+#> Residual deviance: 160.04  on 197  degrees of freedom
+#> AIC: 166.04
+#> 
+#> Number of Fisher Scoring iterations: 6
+#> 
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_lorenz_curve-1.png b/docs/reference/blr_lorenz_curve-1.png index 4a3cb298..a9c441ed 100644 Binary files a/docs/reference/blr_lorenz_curve-1.png and b/docs/reference/blr_lorenz_curve-1.png differ diff --git a/docs/reference/blr_lorenz_curve.html b/docs/reference/blr_lorenz_curve.html index 3c9b25e5..d7491719 100644 --- a/docs/reference/blr_lorenz_curve.html +++ b/docs/reference/blr_lorenz_curve.html @@ -1,238 +1,141 @@ - - - - - - +Lorenz curve — blr_lorenz_curve • blorr + Skip to contents -Lorenz curve — blr_lorenz_curve • blorr +
    +
    +
    - - - +

    Lorenz curve

    + Source: R/blr-lorenz-curve.R +
    blr_lorenz_curve.Rd
    +
    +
    +

    Lorenz curve is a visual representation of inequality. It is + used to measure the discriminatory power of the predictive model.

    +
    +
    +

    Usage

    + 
    blr_lorenz_curve(
+  model,
+  data = NULL,
+  title = "Lorenz Curve",
+  xaxis_title = "Cumulative Events %",
+  yaxis_title = "Cumulative Non Events %",
+  diag_line_col = "red",
+  lorenz_curve_col = "blue",
+  print_plot = TRUE
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    data
    +

    A tibble or data.frame.

    - - - - +
    title
    +

    Plot title.

    +
    xaxis_title
    +

    X axis title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Lorenz curve

    - Source: R/blr-lorenz-curve.R - -
    +
    diag_line_col
    +

    Diagonal line color.

    -
    -

    Lorenz curve is a visual representation of inequality. It is - used to measure the discriminatory power of the predictive model.

    + +
    lorenz_curve_col
    +

    Color of the lorenz curve.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_roc_curve(), +blr_test_hosmer_lemeshow()

    - 
    blr_lorenz_curve(
-  model,
-  data = NULL,
-  title = "Lorenz Curve",
-  xaxis_title = "Cumulative Events %",
-  yaxis_title = "Cumulative Non Events %",
-  diag_line_col = "red",
-  lorenz_curve_col = "blue",
-  print_plot = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    data

    A tibble or data.frame.

    title

    Plot title.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    diag_line_col

    Diagonal line color.

    lorenz_curve_col

    Color of the lorenz curve.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_roc_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_lorenz_curve(model)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_lorenz_curve(model)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_model_fit_stats.html b/docs/reference/blr_model_fit_stats.html index 0b867676..e20af225 100644 --- a/docs/reference/blr_model_fit_stats.html +++ b/docs/reference/blr_model_fit_stats.html @@ -1,230 +1,133 @@ - - - - - - +Model fit statistics — blr_model_fit_stats • blorr + Skip to contents -Model fit statistics — blr_model_fit_stats • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Model fit statistics

    - Source: R/blr-model-fit-stats.R - +
    +

    Model fit statistics.

    -
    -

    Model fit statistics.

    +
    +

    Usage

    + 
    blr_model_fit_stats(model, ...)
    - 
    blr_model_fit_stats(model, ...)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    ...

    Other inputs.

    -

    References

    +
    model
    +

    An object of class glm.

    -

    Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. + +

    ...
    +

    Other inputs.

    + +
    +
    +

    References

    +

    Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. The American Statistician, 54(1), 17-24.

    -

    Windmeijer, F. A. G. (1995). Goodness-of-fit measures in binary choice models. Econometric +

    Windmeijer, F. A. G. (1995). Goodness-of-fit measures in binary choice models. Econometric Reviews, 14, 101-116.

    -

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). +

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). New York: John Wiley & Sons.

    -

    J. Scott Long & Jeremy Freese, 2000. "FITSTAT: Stata module to compute fit statistics for -single equation regression models," Statistical Software Components S407201, Boston College +

    J. Scott Long & Jeremy Freese, 2000. "FITSTAT: Stata module to compute fit statistics for +single equation regression models," Statistical Software Components S407201, Boston College Department of Economics, revised 22 Feb 2001.

    -

    Freese, Jeremy and J. Scott Long. Regression Models for Categorical Dependent Variables +

    Freese, Jeremy and J. Scott Long. Regression Models for Categorical Dependent Variables Using Stata. College Station: Stata Press, 2006.

    -

    Long, J. Scott. Regression Models for Categorical and Limited Dependent Variables. +

    Long, J. Scott. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications, 1997.

    -

    See also

    - -

    Other model fit statistics: -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_model_fit_stats(model)
-#>                               Model Fit Statistics                                
-#> ---------------------------------------------------------------------------------
-#> Log-Lik Intercept Only:      -115.644    Log-Lik Full Model:              -80.118 
-#> Deviance(196):                160.236    LR(3):                            71.052 
-#>                                          Prob > LR:                         0.000 
-#> MCFadden's R2                   0.307    McFadden's Adj R2:                 0.273 
-#> ML (Cox-Snell) R2:              0.299    Cragg-Uhler(Nagelkerke) R2:        0.436 
-#> McKelvey & Zavoina's R2:        0.518    Efron's R2:                        0.330 
-#> Count R2:                       0.810    Adj Count R2:                      0.283 
-#> BIC:                          181.430    AIC:                             168.236 
-#> ---------------------------------------------------------------------------------
-#> 
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model fit statistics: +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_model_fit_stats(model)
+#>                               Model Fit Statistics                                
+#> ---------------------------------------------------------------------------------
+#> Log-Lik Intercept Only:      -115.644    Log-Lik Full Model:              -80.118 
+#> Deviance(196):                160.236    LR(3):                            71.052 
+#>                                          Prob > LR:                         0.000 
+#> MCFadden's R2                   0.307    McFadden's Adj R2:                 0.273 
+#> ML (Cox-Snell) R2:              0.299    Cragg-Uhler(Nagelkerke) R2:        0.436 
+#> McKelvey & Zavoina's R2:        0.518    Efron's R2:                        0.330 
+#> Count R2:                       0.810    Adj Count R2:                      0.283 
+#> BIC:                          181.430    AIC:                             168.236 
+#> ---------------------------------------------------------------------------------
+#> 
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_multi_model_fit_stats.default.html b/docs/reference/blr_multi_model_fit_stats.default.html new file mode 100644 index 00000000..6009925f --- /dev/null +++ b/docs/reference/blr_multi_model_fit_stats.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_multi_model_fit_stats.html b/docs/reference/blr_multi_model_fit_stats.html index c6589405..137eaec9 100644 --- a/docs/reference/blr_multi_model_fit_stats.html +++ b/docs/reference/blr_multi_model_fit_stats.html @@ -1,228 +1,131 @@ - - - - - - +Multi model fit statistics — blr_multi_model_fit_stats • blorr + Skip to contents -Multi model fit statistics — blr_multi_model_fit_stats • blorr +
    +
    +
    +

    Multi model fit statistics

    + Source: R/blr-multi-model-fit-stats.R +
    blr_multi_model_fit_stats.Rd
    +
    +
    +

    Measures of model fit statistics for multiple models.

    +
    +
    +

    Usage

    + 
    blr_multi_model_fit_stats(model, ...)
+
+# Default S3 method
+blr_multi_model_fit_stats(model, ...)
    +
    - - - +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - +
    ...
    +

    Objects of class glm.

    - -
    -
    -
    +
    +

    Value

    +

    A tibble.

    - - -
    -
    - - - - - -
    -
    -
    -

    Multi model fit statistics

    - Source: R/blr-multi-model-fit-stats.R - +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    -
    -

    Measures of model fit statistics for multiple models.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+model2 <- glm(honcomp ~ female + read + math, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_multi_model_fit_stats(model, model2)
+#>                                Measures  Model 1  Model 2
+#> loglik_null      Log-Lik Intercept Only -115.644 -115.644
+#> loglik_model         Log-Lik Full Model  -80.118  -75.210
+#> m_deviance                     Deviance  160.236  150.420
+#> lr_ratio                             LR   71.052   80.869
+#> lr_pval                       Prob > LR    0.000    0.000
+#> mcfadden                  MCFadden's R2    0.307    0.350
+#> adj_mcfadden          McFadden's Adj R2    0.273    0.315
+#> m_aic                 ML (Cox-Snell) R2  168.236  158.420
+#> cox_snell    Cragg-Uhler(Nagelkerke) R2    0.299    0.333
+#> m_bic           McKelvey & Zavoina's R2  181.430  171.613
+#> mckelvey                     Efron's R2    0.518    0.523
+#> effron                         Count R2    0.330    0.379
+#> nagelkerke                 Adj Count R2    0.436    0.485
+#> count_r2                            AIC    0.810    0.830
+#> count_adj                           BIC    0.283    0.358
+
+
    +
    - 
    blr_multi_model_fit_stats(model, ...)
-
-# S3 method for default
-blr_multi_model_fit_stats(model, ...)
    - -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    ...

    Objects of class glm.

    -

    Value

    - -

    A tibble.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-model2 <- glm(honcomp ~ female + read + math, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_multi_model_fit_stats(model, model2)
-#>                                Measures  Model 1  Model 2
-#> loglik_null      Log-Lik Intercept Only -115.644 -115.644
-#> loglik_model         Log-Lik Full Model  -80.118  -75.210
-#> m_deviance                     Deviance  160.236  150.420
-#> lr_ratio                             LR   71.052   80.869
-#> lr_pval                       Prob > LR    0.000    0.000
-#> mcfadden                  MCFadden's R2    0.307    0.350
-#> adj_mcfadden          McFadden's Adj R2    0.273    0.315
-#> m_aic                 ML (Cox-Snell) R2  168.236  158.420
-#> cox_snell    Cragg-Uhler(Nagelkerke) R2    0.299    0.333
-#> m_bic           McKelvey & Zavoina's R2  181.430  171.613
-#> mckelvey                     Efron's R2    0.518    0.523
-#> effron                         Count R2    0.330    0.379
-#> nagelkerke                 Adj Count R2    0.436    0.485
-#> count_r2                            AIC    0.810    0.830
-#> count_adj                           BIC    0.283    0.358
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_pairs.html b/docs/reference/blr_pairs.html index 85650c46..655251e6 100644 --- a/docs/reference/blr_pairs.html +++ b/docs/reference/blr_pairs.html @@ -1,210 +1,109 @@ - - - - - - +Concordant & discordant pairs — blr_pairs • blorr + Skip to contents -Concordant & discordant pairs — blr_pairs • blorr +
    +
    +
    +

    Concordant & discordant pairs

    + Source: R/blr-pairs.R +
    blr_pairs.Rd
    +
    - - - +
    +

    Association of predicted probabilities and observed responses.

    +
    - +
    +

    Usage

    + 
    blr_pairs(model)
    +
    +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - -
    -
    -
    +
    +

    Value

    +

    A tibble.

    - - -
    -
    - - - - - -
    -
    -
    -

    Concordant & discordant pairs

    - Source: R/blr-pairs.R - +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    -
    -

    Association of predicted probabilities and observed responses.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_pairs(model)
+#>   pairs concordance discordance        tied somers_d     gamma      tau
+#> 1  7791    0.856116   0.1424721 0.001411886 0.714653 0.7136439 0.279397
+#>          c
+#> 1 0.856822
+
+
    +
    - 
    blr_pairs(model)
    - -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    - -

    A tibble.

    -

    References

    - -

    https://doi.org/10.1080/10485259808832744

    -

    https://doi.org/10.1177/1536867X0600600302

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_pairs(model)
-#>   pairs concordance discordance        tied somers_d     gamma      tau
-#> 1  7791    0.856116   0.1424721 0.001411886 0.714653 0.7136439 0.279397
-#>          c
-#> 1 0.856822
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_plot_c_fitted-1.png b/docs/reference/blr_plot_c_fitted-1.png index 92168bfc..09f19d27 100644 Binary files a/docs/reference/blr_plot_c_fitted-1.png and b/docs/reference/blr_plot_c_fitted-1.png differ diff --git a/docs/reference/blr_plot_c_fitted.html b/docs/reference/blr_plot_c_fitted.html index c989ac3a..854d5117 100644 --- a/docs/reference/blr_plot_c_fitted.html +++ b/docs/reference/blr_plot_c_fitted.html @@ -1,210 +1,111 @@ - - - - - - +CI Displacement C vs fitted values plot — blr_plot_c_fitted • blorr + Skip to contents -CI Displacement C vs fitted values plot — blr_plot_c_fitted • blorr +
    +
    +
    - - - +

    CI Displacement C vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_c_fitted.Rd
    +
    +
    +

    Confidence interval displacement diagnostics C vs fitted values plot.

    +
    +
    +

    Usage

    + 
    blr_plot_c_fitted(
+  model,
+  point_color = "blue",
+  title = "CI Displacement C vs Fitted Values Plot",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "CI Displacement C"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    CI Displacement C vs fitted values plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_c_fitted(model)
+
+
+
    +
    -
    -

    Confidence interval displacement diagnostics C vs fitted values plot.

    -
    - 
    blr_plot_c_fitted(
-  model,
-  point_color = "blue",
-  title = "CI Displacement C vs Fitted Values Plot",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "CI Displacement C"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_c_fitted(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_c_leverage-1.png b/docs/reference/blr_plot_c_leverage-1.png index bfca91f3..8cec28f7 100644 Binary files a/docs/reference/blr_plot_c_leverage-1.png and b/docs/reference/blr_plot_c_leverage-1.png differ diff --git a/docs/reference/blr_plot_c_leverage.html b/docs/reference/blr_plot_c_leverage.html index 5c07a5e5..5b525fa7 100644 --- a/docs/reference/blr_plot_c_leverage.html +++ b/docs/reference/blr_plot_c_leverage.html @@ -1,210 +1,111 @@ - - - - - - +CI Displacement C vs leverage plot — blr_plot_c_leverage • blorr + Skip to contents -CI Displacement C vs leverage plot — blr_plot_c_leverage • blorr +
    +
    +
    - - - +

    CI Displacement C vs leverage plot

    + Source: R/blr-plots.R +
    blr_plot_c_leverage.Rd
    +
    +
    +

    Confidence interval displacement diagnostics C vs leverage plot.

    +
    +
    +

    Usage

    + 
    blr_plot_c_leverage(
+  model,
+  point_color = "blue",
+  title = "CI Displacement C vs Leverage Plot",
+  xaxis_title = "Leverage",
+  yaxis_title = "CI Displacement C"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    CI Displacement C vs leverage plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_c_leverage(model)
+
+
+
    +
    -
    -

    Confidence interval displacement diagnostics C vs leverage plot.

    -
    - 
    blr_plot_c_leverage(
-  model,
-  point_color = "blue",
-  title = "CI Displacement C vs Leverage Plot",
-  xaxis_title = "Leverage",
-  yaxis_title = "CI Displacement C"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_c_leverage(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_deviance_fitted-1.png b/docs/reference/blr_plot_deviance_fitted-1.png index 4d9aa8d7..0845a4d5 100644 Binary files a/docs/reference/blr_plot_deviance_fitted-1.png and b/docs/reference/blr_plot_deviance_fitted-1.png differ diff --git a/docs/reference/blr_plot_deviance_fitted.html b/docs/reference/blr_plot_deviance_fitted.html index dd0cc8df..ed2a7cb1 100644 --- a/docs/reference/blr_plot_deviance_fitted.html +++ b/docs/reference/blr_plot_deviance_fitted.html @@ -1,215 +1,116 @@ - - - - - - +Deviance vs fitted values plot — blr_plot_deviance_fitted • blorr + Skip to contents -Deviance vs fitted values plot — blr_plot_deviance_fitted • blorr +
    +
    +
    - - - +

    Deviance vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_deviance_fitted.Rd
    +
    +
    +

    Deviance vs fitted values plot.

    +
    +
    +

    Usage

    + 
    blr_plot_deviance_fitted(
+  model,
+  point_color = "blue",
+  line_color = "red",
+  title = "Deviance Residual vs Fitted Values",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "Deviance Residual"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    line_color
    +

    Color of the horizontal line.

    +
    title
    +

    Title of the plot.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Deviance vs fitted values plot

    - Source: R/blr-plots.R - -
    +
    yaxis_title
    +

    Y axis label.

    -
    -

    Deviance vs fitted values plot.

    -
    +
    - 
    blr_plot_deviance_fitted(
-  model,
-  point_color = "blue",
-  line_color = "red",
-  title = "Deviance Residual vs Fitted Values",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "Deviance Residual"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    line_color

    Color of the horizontal line.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_deviance_fitted(model)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_deviance_fitted(model)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_plot_deviance_residual-1.png b/docs/reference/blr_plot_deviance_residual-1.png index bb68c973..fea391e8 100644 Binary files a/docs/reference/blr_plot_deviance_residual-1.png and b/docs/reference/blr_plot_deviance_residual-1.png differ diff --git a/docs/reference/blr_plot_deviance_residual.html b/docs/reference/blr_plot_deviance_residual.html index 0d678f5d..664b31e8 100644 --- a/docs/reference/blr_plot_deviance_residual.html +++ b/docs/reference/blr_plot_deviance_residual.html @@ -1,210 +1,111 @@ - - - - - - +Deviance residual values — blr_plot_deviance_residual • blorr + Skip to contents -Deviance residual values — blr_plot_deviance_residual • blorr +
    +
    +
    - - - +

    Deviance residual values

    + Source: R/blr-plots.R +
    blr_plot_deviance_residual.Rd
    +
    +
    +

    Deviance residuals plot.

    +
    +
    +

    Usage

    + 
    blr_plot_deviance_residual(
+  model,
+  point_color = "blue",
+  title = "Deviance Residuals Plot",
+  xaxis_title = "id",
+  yaxis_title = "Deviance Residuals"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Deviance residual values

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_deviance_residual(model)
+
+
+
    +
    -
    -

    Deviance residuals plot.

    -
    - 
    blr_plot_deviance_residual(
-  model,
-  point_color = "blue",
-  title = "Deviance Residuals Plot",
-  xaxis_title = "id",
-  yaxis_title = "Deviance Residuals"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_deviance_residual(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_dfbetas_panel.html b/docs/reference/blr_plot_dfbetas_panel.html index 79ffd72e..f848b29a 100644 --- a/docs/reference/blr_plot_dfbetas_panel.html +++ b/docs/reference/blr_plot_dfbetas_panel.html @@ -1,166 +1,74 @@ - - - - - - +DFBETAs panel — blr_plot_dfbetas_panel • blorr + Skip to contents -DFBETAs panel — blr_plot_dfbetas_panel • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    DFBETAs panel

    - Source: R/blr-plots.R - +
    +

    Panel of plots to detect influential observations using DFBETAs.

    -
    -

    Panel of plots to detect influential observations using DFBETAs.

    +
    +

    Usage

    + 
    blr_plot_dfbetas_panel(model, print_plot = TRUE)
    - 
    blr_plot_dfbetas_panel(model, print_plot = TRUE)
    +
    +

    Arguments

    + -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    +
    model
    +

    An object of class glm.

    -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    Value

    list; blr_dfbetas_panel returns a list of tibbles (for intercept and each predictor) with the observation number and DFBETA of observations that exceed the threshold for classifying an observation as an outlier/influential observation.

    -

    Details

    - +
    +
    +

    Details

    DFBETA measures the difference in each parameter estimate with and without the influential point. There is a DFBETA for each data point i.e if there are n observations and k variables, there will be \(n * k\) DFBETAs. In @@ -168,47 +76,43 @@

    Details in estimating a given parameter. Belsley, Kuh, and Welsch recommend 2 as a general cutoff value to indicate influential observations and \(2/\sqrt(n)\) as a size-adjusted cutoff.

    -

    References

    - +
    +
    +

    References

    Belsley, David A.; Kuh, Edwin; Welsh, Roy E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons. pp. ISBN 0-471-05856-4.

    +
    -

    Examples

    - 
    if (FALSE) {
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_dfbetas_panel(model)
-}
-
-
    -
    - -
    +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_dfbetas_panel(model)
+} # }
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_plot_diag_c-1.png b/docs/reference/blr_plot_diag_c-1.png index 6b6e9817..f162d71e 100644 Binary files a/docs/reference/blr_plot_diag_c-1.png and b/docs/reference/blr_plot_diag_c-1.png differ diff --git a/docs/reference/blr_plot_diag_c.html b/docs/reference/blr_plot_diag_c.html index 9d1d46b3..b88725f1 100644 --- a/docs/reference/blr_plot_diag_c.html +++ b/docs/reference/blr_plot_diag_c.html @@ -1,210 +1,111 @@ - - - - - - +CI Displacement C plot — blr_plot_diag_c • blorr + Skip to contents -CI Displacement C plot — blr_plot_diag_c • blorr +
    +
    +
    - - - +

    CI Displacement C plot

    + Source: R/blr-plots.R +
    blr_plot_diag_c.Rd
    +
    +
    +

    Confidence interval displacement diagnostics C plot.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_c(
+  model,
+  point_color = "blue",
+  title = "CI Displacement C Plot",
+  xaxis_title = "id",
+  yaxis_title = "CI Displacement C"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    CI Displacement C plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_c(model)
+
+
+
    +
    -
    -

    Confidence interval displacement diagnostics C plot.

    -
    - 
    blr_plot_diag_c(
-  model,
-  point_color = "blue",
-  title = "CI Displacement C Plot",
-  xaxis_title = "id",
-  yaxis_title = "CI Displacement C"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_c(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_diag_cbar-1.png b/docs/reference/blr_plot_diag_cbar-1.png index c0ec33b7..958a36ee 100644 Binary files a/docs/reference/blr_plot_diag_cbar-1.png and b/docs/reference/blr_plot_diag_cbar-1.png differ diff --git a/docs/reference/blr_plot_diag_cbar.html b/docs/reference/blr_plot_diag_cbar.html index 4122c45e..3c22dfb5 100644 --- a/docs/reference/blr_plot_diag_cbar.html +++ b/docs/reference/blr_plot_diag_cbar.html @@ -1,210 +1,111 @@ - - - - - - +CI Displacement CBAR plot — blr_plot_diag_cbar • blorr + Skip to contents -CI Displacement CBAR plot — blr_plot_diag_cbar • blorr +
    +
    +
    - - - +

    CI Displacement CBAR plot

    + Source: R/blr-plots.R +
    blr_plot_diag_cbar.Rd
    +
    +
    +

    Confidence interval displacement diagnostics CBAR plot.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_cbar(
+  model,
+  point_color = "blue",
+  title = "CI Displacement CBAR Plot",
+  xaxis_title = "id",
+  yaxis_title = "CI Displacement CBAR"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    CI Displacement CBAR plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_cbar(model)
+
+
+
    +
    -
    -

    Confidence interval displacement diagnostics CBAR plot.

    -
    - 
    blr_plot_diag_cbar(
-  model,
-  point_color = "blue",
-  title = "CI Displacement CBAR Plot",
-  xaxis_title = "id",
-  yaxis_title = "CI Displacement CBAR"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_cbar(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_diag_difchisq-1.png b/docs/reference/blr_plot_diag_difchisq-1.png index 512f9c7e..28582667 100644 Binary files a/docs/reference/blr_plot_diag_difchisq-1.png and b/docs/reference/blr_plot_diag_difchisq-1.png differ diff --git a/docs/reference/blr_plot_diag_difchisq.html b/docs/reference/blr_plot_diag_difchisq.html index fdfd19c0..658d02de 100644 --- a/docs/reference/blr_plot_diag_difchisq.html +++ b/docs/reference/blr_plot_diag_difchisq.html @@ -1,210 +1,111 @@ - - - - - - +Delta chisquare plot — blr_plot_diag_difchisq • blorr + Skip to contents -Delta chisquare plot — blr_plot_diag_difchisq • blorr +
    +
    +
    - - - +

    Delta chisquare plot

    + Source: R/blr-plots.R +
    blr_plot_diag_difchisq.Rd
    +
    +
    +

    Diagnostics for detecting ill fitted observations.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_difchisq(
+  model,
+  point_color = "blue",
+  title = "Delta Chisquare Plot",
+  xaxis_title = "id",
+  yaxis_title = "Delta Chisquare"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta chisquare plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_difchisq(model)
+
+
+
    +
    -
    -

    Diagnostics for detecting ill fitted observations.

    -
    - 
    blr_plot_diag_difchisq(
-  model,
-  point_color = "blue",
-  title = "Delta Chisquare Plot",
-  xaxis_title = "id",
-  yaxis_title = "Delta Chisquare"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_difchisq(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_diag_difdev-1.png b/docs/reference/blr_plot_diag_difdev-1.png index e4931dac..acc8538d 100644 Binary files a/docs/reference/blr_plot_diag_difdev-1.png and b/docs/reference/blr_plot_diag_difdev-1.png differ diff --git a/docs/reference/blr_plot_diag_difdev.html b/docs/reference/blr_plot_diag_difdev.html index bcd0a402..9d207a26 100644 --- a/docs/reference/blr_plot_diag_difdev.html +++ b/docs/reference/blr_plot_diag_difdev.html @@ -1,210 +1,111 @@ - - - - - - +Delta deviance plot — blr_plot_diag_difdev • blorr + Skip to contents -Delta deviance plot — blr_plot_diag_difdev • blorr +
    +
    +
    - - - +

    Delta deviance plot

    + Source: R/blr-plots.R +
    blr_plot_diag_difdev.Rd
    +
    +
    +

    Diagnostics for detecting ill fitted observations.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_difdev(
+  model,
+  point_color = "blue",
+  title = "Delta Deviance Plot",
+  xaxis_title = "id",
+  yaxis_title = "Delta Deviance"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta deviance plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_difdev(model)
+
+
+
    +
    -
    -

    Diagnostics for detecting ill fitted observations.

    -
    - 
    blr_plot_diag_difdev(
-  model,
-  point_color = "blue",
-  title = "Delta Deviance Plot",
-  xaxis_title = "id",
-  yaxis_title = "Delta Deviance"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_difdev(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_diag_fit-1.png b/docs/reference/blr_plot_diag_fit-1.png index 7f26f370..a24da493 100644 Binary files a/docs/reference/blr_plot_diag_fit-1.png and b/docs/reference/blr_plot_diag_fit-1.png differ diff --git a/docs/reference/blr_plot_diag_fit.html b/docs/reference/blr_plot_diag_fit.html index f429b061..9e684508 100644 --- a/docs/reference/blr_plot_diag_fit.html +++ b/docs/reference/blr_plot_diag_fit.html @@ -1,204 +1,108 @@ - - - - - - +Fitted values diagnostics plot — blr_plot_diag_fit • blorr + Skip to contents -Fitted values diagnostics plot — blr_plot_diag_fit • blorr +
    +
    +
    +

    Fitted values diagnostics plot

    + Source: R/blr-residual-diagnostics.R +
    blr_plot_diag_fit.Rd
    +
    +
    +

    Diagnostic plots for fitted values.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_fit(model, print_plot = TRUE)
    +
    - - - +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    - -
    -
    -
    +
    +

    Value

    +

    A panel of diagnostic plots for fitted values.

    - - -
    -
    - - - - - -
    -
    -
    -

    Fitted values diagnostics plot

    - Source: R/blr-residual-diagnostics.R - +
    +

    References

    +

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    +

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    - -
    -

    Diagnostic plots for fitted values.

    +
    +

    See also

    +

    Other diagnostic plots: +blr_plot_diag_influence(), +blr_plot_diag_leverage()

    - 
    blr_plot_diag_fit(model, print_plot = TRUE)
    - -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_fit(model)
+
+
+
    +
    +
    -

    A panel of diagnostic plots for fitted values.

    -

    References

    -

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    -

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    -

    See also

    - -

    Other diagnostic plots: -blr_plot_diag_influence(), -blr_plot_diag_leverage()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_fit(model)
-
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_plot_diag_influence-1.png b/docs/reference/blr_plot_diag_influence-1.png index bb0f9be4..b9bb0e6c 100644 Binary files a/docs/reference/blr_plot_diag_influence-1.png and b/docs/reference/blr_plot_diag_influence-1.png differ diff --git a/docs/reference/blr_plot_diag_influence.html b/docs/reference/blr_plot_diag_influence.html index 8c2c45df..c9ad20e2 100644 --- a/docs/reference/blr_plot_diag_influence.html +++ b/docs/reference/blr_plot_diag_influence.html @@ -1,204 +1,108 @@ - - - - - - +Influence diagnostics plot — blr_plot_diag_influence • blorr + Skip to contents -Influence diagnostics plot — blr_plot_diag_influence • blorr +
    +
    +
    +

    Influence diagnostics plot

    + Source: R/blr-residual-diagnostics.R +
    blr_plot_diag_influence.Rd
    +
    +
    +

    Reisudal diagnostic plots for detecting influential observations.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_influence(model, print_plot = TRUE)
    +
    - - - +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    - -
    -
    -
    +
    +

    Value

    +

    A panel of influence diagnostic plots.

    - - -
    -
    - - - - - -
    -
    -
    -

    Influence diagnostics plot

    - Source: R/blr-residual-diagnostics.R - +
    +

    References

    +

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    +

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    - -
    -

    Reisudal diagnostic plots for detecting influential observations.

    +
    +

    See also

    +

    Other diagnostic plots: +blr_plot_diag_fit(), +blr_plot_diag_leverage()

    - 
    blr_plot_diag_influence(model, print_plot = TRUE)
    - -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_influence(model)
+
+
+
    +
    +
    -

    A panel of influence diagnostic plots.

    -

    References

    -

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    -

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    -

    See also

    - -

    Other diagnostic plots: -blr_plot_diag_fit(), -blr_plot_diag_leverage()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_influence(model)
-
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_plot_diag_leverage-1.png b/docs/reference/blr_plot_diag_leverage-1.png index 9d19ebdf..f9dcfb5a 100644 Binary files a/docs/reference/blr_plot_diag_leverage-1.png and b/docs/reference/blr_plot_diag_leverage-1.png differ diff --git a/docs/reference/blr_plot_diag_leverage.html b/docs/reference/blr_plot_diag_leverage.html index 22e24942..6efb2020 100644 --- a/docs/reference/blr_plot_diag_leverage.html +++ b/docs/reference/blr_plot_diag_leverage.html @@ -1,204 +1,108 @@ - - - - - - +Leverage diagnostics plot — blr_plot_diag_leverage • blorr + Skip to contents -Leverage diagnostics plot — blr_plot_diag_leverage • blorr +
    +
    +
    +

    Leverage diagnostics plot

    + Source: R/blr-residual-diagnostics.R +
    blr_plot_diag_leverage.Rd
    +
    +
    +

    Diagnostic plots for leverage.

    +
    +
    +

    Usage

    + 
    blr_plot_diag_leverage(model, print_plot = TRUE)
    +
    - - - +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    - -
    -
    -
    +
    +

    Value

    +

    A panel of diagnostic plots for leverage.

    - - -
    -
    - - - - - -
    -
    -
    -

    Leverage diagnostics plot

    - Source: R/blr-residual-diagnostics.R - +
    +

    References

    +

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    +

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    - -
    -

    Diagnostic plots for leverage.

    +
    +

    See also

    +

    Other diagnostic plots: +blr_plot_diag_fit(), +blr_plot_diag_influence()

    - 
    blr_plot_diag_leverage(model, print_plot = TRUE)
    - -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_diag_leverage(model)
+
+
+
    +
    +
    -

    A panel of diagnostic plots for leverage.

    -

    References

    -

    Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications.

    -

    Cook, R. D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman & Hall.

    -

    See also

    - -

    Other diagnostic plots: -blr_plot_diag_fit(), -blr_plot_diag_influence()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_diag_leverage(model)
-
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_plot_difchisq_fitted-1.png b/docs/reference/blr_plot_difchisq_fitted-1.png index 76f35ca4..4dfdb573 100644 Binary files a/docs/reference/blr_plot_difchisq_fitted-1.png and b/docs/reference/blr_plot_difchisq_fitted-1.png differ diff --git a/docs/reference/blr_plot_difchisq_fitted.html b/docs/reference/blr_plot_difchisq_fitted.html index 71fe9bdf..086711aa 100644 --- a/docs/reference/blr_plot_difchisq_fitted.html +++ b/docs/reference/blr_plot_difchisq_fitted.html @@ -1,210 +1,111 @@ - - - - - - +Delta chi square vs fitted values plot — blr_plot_difchisq_fitted • blorr + Skip to contents -Delta chi square vs fitted values plot — blr_plot_difchisq_fitted • blorr +
    +
    +
    - - - +

    Delta chi square vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_difchisq_fitted.Rd
    +
    +
    +

    Delta Chi Square vs fitted values plot for detecting ill fitted observations.

    +
    +
    +

    Usage

    + 
    blr_plot_difchisq_fitted(
+  model,
+  point_color = "blue",
+  title = "Delta Chi Square vs Fitted Values Plot",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "Delta Chi Square"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta chi square vs fitted values plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_difchisq_fitted(model)
+
+
+
    +
    -
    -

    Delta Chi Square vs fitted values plot for detecting ill fitted observations.

    -
    - 
    blr_plot_difchisq_fitted(
-  model,
-  point_color = "blue",
-  title = "Delta Chi Square vs Fitted Values Plot",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "Delta Chi Square"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_difchisq_fitted(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_difchisq_leverage-1.png b/docs/reference/blr_plot_difchisq_leverage-1.png index 8243bbb8..828fcac3 100644 Binary files a/docs/reference/blr_plot_difchisq_leverage-1.png and b/docs/reference/blr_plot_difchisq_leverage-1.png differ diff --git a/docs/reference/blr_plot_difchisq_leverage.html b/docs/reference/blr_plot_difchisq_leverage.html index f482a1eb..170bf14f 100644 --- a/docs/reference/blr_plot_difchisq_leverage.html +++ b/docs/reference/blr_plot_difchisq_leverage.html @@ -1,210 +1,111 @@ - - - - - - +Delta chi square vs leverage plot — blr_plot_difchisq_leverage • blorr + Skip to contents -Delta chi square vs leverage plot — blr_plot_difchisq_leverage • blorr +
    +
    +
    - - - +

    Delta chi square vs leverage plot

    + Source: R/blr-plots.R +
    blr_plot_difchisq_leverage.Rd
    +
    +
    +

    Delta chi square vs leverage plot.

    +
    +
    +

    Usage

    + 
    blr_plot_difchisq_leverage(
+  model,
+  point_color = "blue",
+  title = "Delta Chi Square vs Leverage Plot",
+  xaxis_title = "Leverage",
+  yaxis_title = "Delta Chi Square"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta chi square vs leverage plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_difchisq_leverage(model)
+
+
+
    +
    -
    -

    Delta chi square vs leverage plot.

    -
    - 
    blr_plot_difchisq_leverage(
-  model,
-  point_color = "blue",
-  title = "Delta Chi Square vs Leverage Plot",
-  xaxis_title = "Leverage",
-  yaxis_title = "Delta Chi Square"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_difchisq_leverage(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_difdev_fitted-1.png b/docs/reference/blr_plot_difdev_fitted-1.png index 08b7b3ff..7faa9728 100644 Binary files a/docs/reference/blr_plot_difdev_fitted-1.png and b/docs/reference/blr_plot_difdev_fitted-1.png differ diff --git a/docs/reference/blr_plot_difdev_fitted.html b/docs/reference/blr_plot_difdev_fitted.html index eb804fba..6f2ea3cd 100644 --- a/docs/reference/blr_plot_difdev_fitted.html +++ b/docs/reference/blr_plot_difdev_fitted.html @@ -1,210 +1,111 @@ - - - - - - +Delta deviance vs fitted values plot — blr_plot_difdev_fitted • blorr + Skip to contents -Delta deviance vs fitted values plot — blr_plot_difdev_fitted • blorr +
    +
    +
    - - - +

    Delta deviance vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_difdev_fitted.Rd
    +
    +
    +

    Delta deviance vs fitted values plot for detecting ill fitted observations.

    +
    +
    +

    Usage

    + 
    blr_plot_difdev_fitted(
+  model,
+  point_color = "blue",
+  title = "Delta Deviance vs Fitted Values Plot",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "Delta Deviance"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta deviance vs fitted values plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_difdev_fitted(model)
+
+
+
    +
    -
    -

    Delta deviance vs fitted values plot for detecting ill fitted observations.

    -
    - 
    blr_plot_difdev_fitted(
-  model,
-  point_color = "blue",
-  title = "Delta Deviance vs Fitted Values Plot",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "Delta Deviance"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_difdev_fitted(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_difdev_leverage-1.png b/docs/reference/blr_plot_difdev_leverage-1.png index 5052a438..b9daf63c 100644 Binary files a/docs/reference/blr_plot_difdev_leverage-1.png and b/docs/reference/blr_plot_difdev_leverage-1.png differ diff --git a/docs/reference/blr_plot_difdev_leverage.html b/docs/reference/blr_plot_difdev_leverage.html index f4d99e20..b438d73b 100644 --- a/docs/reference/blr_plot_difdev_leverage.html +++ b/docs/reference/blr_plot_difdev_leverage.html @@ -1,210 +1,111 @@ - - - - - - +Delta deviance vs leverage plot — blr_plot_difdev_leverage • blorr + Skip to contents -Delta deviance vs leverage plot — blr_plot_difdev_leverage • blorr +
    +
    +
    - - - +

    Delta deviance vs leverage plot

    + Source: R/blr-plots.R +
    blr_plot_difdev_leverage.Rd
    +
    +
    +

    Delta deviance vs leverage plot.

    +
    +
    +

    Usage

    + 
    blr_plot_difdev_leverage(
+  model,
+  point_color = "blue",
+  title = "Delta Deviance vs Leverage Plot",
+  xaxis_title = "Leverage",
+  yaxis_title = "Delta Deviance"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Delta deviance vs leverage plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_difdev_leverage(model)
+
+
+
    +
    -
    -

    Delta deviance vs leverage plot.

    -
    - 
    blr_plot_difdev_leverage(
-  model,
-  point_color = "blue",
-  title = "Delta Deviance vs Leverage Plot",
-  xaxis_title = "Leverage",
-  yaxis_title = "Delta Deviance"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_difdev_leverage(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_fitted_leverage-1.png b/docs/reference/blr_plot_fitted_leverage-1.png index 9dd1e679..b56eb19e 100644 Binary files a/docs/reference/blr_plot_fitted_leverage-1.png and b/docs/reference/blr_plot_fitted_leverage-1.png differ diff --git a/docs/reference/blr_plot_fitted_leverage.html b/docs/reference/blr_plot_fitted_leverage.html index 3cf3fc3c..fa11343a 100644 --- a/docs/reference/blr_plot_fitted_leverage.html +++ b/docs/reference/blr_plot_fitted_leverage.html @@ -1,210 +1,111 @@ - - - - - - +Fitted values vs leverage plot — blr_plot_fitted_leverage • blorr + Skip to contents -Fitted values vs leverage plot — blr_plot_fitted_leverage • blorr +
    +
    +
    - - - +

    Fitted values vs leverage plot

    + Source: R/blr-plots.R +
    blr_plot_fitted_leverage.Rd
    +
    +
    +

    Fitted values vs leverage plot.

    +
    +
    +

    Usage

    + 
    blr_plot_fitted_leverage(
+  model,
+  point_color = "blue",
+  title = "Fitted Values vs Leverage Plot",
+  xaxis_title = "Leverage",
+  yaxis_title = "Fitted Values"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Fitted values vs leverage plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_fitted_leverage(model)
+
+
+
    +
    -
    -

    Fitted values vs leverage plot.

    -
    - 
    blr_plot_fitted_leverage(
-  model,
-  point_color = "blue",
-  title = "Fitted Values vs Leverage Plot",
-  xaxis_title = "Leverage",
-  yaxis_title = "Fitted Values"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_fitted_leverage(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_leverage-1.png b/docs/reference/blr_plot_leverage-1.png index 8476a99f..45dcce77 100644 Binary files a/docs/reference/blr_plot_leverage-1.png and b/docs/reference/blr_plot_leverage-1.png differ diff --git a/docs/reference/blr_plot_leverage.html b/docs/reference/blr_plot_leverage.html index 354b250c..0b2139bf 100644 --- a/docs/reference/blr_plot_leverage.html +++ b/docs/reference/blr_plot_leverage.html @@ -1,210 +1,111 @@ - - - - - - +Leverage plot — blr_plot_leverage • blorr + Skip to contents -Leverage plot — blr_plot_leverage • blorr +
    +
    +
    - - - +

    Leverage plot

    + Source: R/blr-plots.R +
    blr_plot_leverage.Rd
    +
    +
    +

    Leverage plot.

    +
    +
    +

    Usage

    + 
    blr_plot_leverage(
+  model,
+  point_color = "blue",
+  title = "Leverage Plot",
+  xaxis_title = "id",
+  yaxis_title = "Leverage"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Leverage plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_leverage(model)
+
+
+
    +
    -
    -

    Leverage plot.

    -
    - 
    blr_plot_leverage(
-  model,
-  point_color = "blue",
-  title = "Leverage Plot",
-  xaxis_title = "id",
-  yaxis_title = "Leverage"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_leverage(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_leverage_fitted-1.png b/docs/reference/blr_plot_leverage_fitted-1.png index a7436253..b4222b05 100644 Binary files a/docs/reference/blr_plot_leverage_fitted-1.png and b/docs/reference/blr_plot_leverage_fitted-1.png differ diff --git a/docs/reference/blr_plot_leverage_fitted.html b/docs/reference/blr_plot_leverage_fitted.html index 5ceadabc..52f019ca 100644 --- a/docs/reference/blr_plot_leverage_fitted.html +++ b/docs/reference/blr_plot_leverage_fitted.html @@ -1,210 +1,111 @@ - - - - - - +Leverage vs fitted values plot — blr_plot_leverage_fitted • blorr + Skip to contents -Leverage vs fitted values plot — blr_plot_leverage_fitted • blorr +
    +
    +
    - - - +

    Leverage vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_leverage_fitted.Rd
    +
    +
    +

    Leverage vs fitted values plot

    +
    +
    +

    Usage

    + 
    blr_plot_leverage_fitted(
+  model,
+  point_color = "blue",
+  title = "Leverage vs Fitted Values",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "Leverage"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Leverage vs fitted values plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_leverage_fitted(model)
+
+
+
    +
    -
    -

    Leverage vs fitted values plot

    -
    - 
    blr_plot_leverage_fitted(
-  model,
-  point_color = "blue",
-  title = "Leverage vs Fitted Values",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "Leverage"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_leverage_fitted(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_pearson_residual-1.png b/docs/reference/blr_plot_pearson_residual-1.png index 3306be0b..0d3e48d4 100644 Binary files a/docs/reference/blr_plot_pearson_residual-1.png and b/docs/reference/blr_plot_pearson_residual-1.png differ diff --git a/docs/reference/blr_plot_pearson_residual.html b/docs/reference/blr_plot_pearson_residual.html index d8c60205..5b816ec9 100644 --- a/docs/reference/blr_plot_pearson_residual.html +++ b/docs/reference/blr_plot_pearson_residual.html @@ -1,210 +1,111 @@ - - - - - - +Residual values plot — blr_plot_pearson_residual • blorr + Skip to contents -Residual values plot — blr_plot_pearson_residual • blorr +
    +
    +
    - - - +

    Residual values plot

    + Source: R/blr-plots.R +
    blr_plot_pearson_residual.Rd
    +
    +
    +

    Standardised pearson residuals plot.

    +
    +
    +

    Usage

    + 
    blr_plot_pearson_residual(
+  model,
+  point_color = "blue",
+  title = "Standardized Pearson Residuals",
+  xaxis_title = "id",
+  yaxis_title = "Standardized Pearson Residuals"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    title
    +

    Title of the plot.

    +
    xaxis_title
    +

    X axis label.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Residual values plot

    - Source: R/blr-plots.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_pearson_residual(model)
+
+
+
    +
    -
    -

    Standardised pearson residuals plot.

    -
    - 
    blr_plot_pearson_residual(
-  model,
-  point_color = "blue",
-  title = "Standardized Pearson Residuals",
-  xaxis_title = "id",
-  yaxis_title = "Standardized Pearson Residuals"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_pearson_residual(model)
-
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_plot_residual_fitted-1.png b/docs/reference/blr_plot_residual_fitted-1.png index a499cf79..95f50f77 100644 Binary files a/docs/reference/blr_plot_residual_fitted-1.png and b/docs/reference/blr_plot_residual_fitted-1.png differ diff --git a/docs/reference/blr_plot_residual_fitted.html b/docs/reference/blr_plot_residual_fitted.html index 94ce6f5b..a9e7b958 100644 --- a/docs/reference/blr_plot_residual_fitted.html +++ b/docs/reference/blr_plot_residual_fitted.html @@ -1,215 +1,116 @@ - - - - - - +Residual vs fitted values plot — blr_plot_residual_fitted • blorr + Skip to contents -Residual vs fitted values plot — blr_plot_residual_fitted • blorr +
    +
    +
    - - - +

    Residual vs fitted values plot

    + Source: R/blr-plots.R +
    blr_plot_residual_fitted.Rd
    +
    +
    +

    Residual vs fitted values plot.

    +
    +
    +

    Usage

    + 
    blr_plot_residual_fitted(
+  model,
+  point_color = "blue",
+  line_color = "red",
+  title = "Standardized Pearson Residual vs Fitted Values",
+  xaxis_title = "Fitted Values",
+  yaxis_title = "Standardized Pearson Residual"
+)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    point_color
    +

    Color of the points.

    - - - - +
    line_color
    +

    Color of the horizontal line.

    +
    title
    +

    Title of the plot.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Residual vs fitted values plot

    - Source: R/blr-plots.R - -
    +
    yaxis_title
    +

    Y axis label.

    -
    -

    Residual vs fitted values plot.

    -
    +
    - 
    blr_plot_residual_fitted(
-  model,
-  point_color = "blue",
-  line_color = "red",
-  title = "Standardized Pearson Residual vs Fitted Values",
-  xaxis_title = "Fitted Values",
-  yaxis_title = "Standardized Pearson Residual"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    point_color

    Color of the points.

    line_color

    Color of the horizontal line.

    title

    Title of the plot.

    xaxis_title

    X axis label.

    yaxis_title

    Y axis label.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_plot_residual_fitted(model)
-
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_plot_residual_fitted(model)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_prep_dcrate_data.html b/docs/reference/blr_prep_dcrate_data.html index 4917226e..c606c5ed 100644 --- a/docs/reference/blr_prep_dcrate_data.html +++ b/docs/reference/blr_prep_dcrate_data.html @@ -1,198 +1,99 @@ - - - - - - +Decile capture rate data — blr_prep_dcrate_data • blorr + Skip to contents -Decile capture rate data — blr_prep_dcrate_data • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Decile capture rate data

    - Source: R/blr-plots-data.R - +
    +

    Data for generating decile capture rate.

    -
    -

    Data for generating decile capture rate.

    +
    +

    Usage

    + 
    blr_prep_dcrate_data(gains_table)
    - 
    blr_prep_dcrate_data(gains_table)
    - -

    Arguments

    - - - - - - -
    gains_table

    An object of clas blr_gains_table

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_prep_dcrate_data(gt)
-#>    decile total  1 decile_mean
-#> 1       1    20 14        0.70
-#> 2       2    20 13        0.65
-#> 3       3    20 10        0.50
-#> 4       4    20  7        0.35
-#> 5       5    20  3        0.15
-#> 6       6    20  3        0.15
-#> 7       7    20  1        0.05
-#> 8       8    20  2        0.10
-#> 9       9    20  0        0.00
-#> 10     10    20  0        0.00
-
-
    -
    - -
    +
    +

    Arguments

    + + +
    gains_table
    +

    An object of clas blr_gains_table

    + +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_prep_dcrate_data(gt)
+#>    decile total  1 decile_mean
+#> 1       1    20 14        0.70
+#> 2       2    20 13        0.65
+#> 3       3    20 10        0.50
+#> 4       4    20  7        0.35
+#> 5       5    20  3        0.15
+#> 6       6    20  3        0.15
+#> 7       7    20  1        0.05
+#> 8       8    20  2        0.10
+#> 9       9    20  0        0.00
+#> 10     10    20  0        0.00
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_prep_ksannotate_x.html b/docs/reference/blr_prep_ksannotate_x.html new file mode 100644 index 00000000..785ddb47 --- /dev/null +++ b/docs/reference/blr_prep_ksannotate_x.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_prep_ksannotate_y.html b/docs/reference/blr_prep_ksannotate_y.html new file mode 100644 index 00000000..785ddb47 --- /dev/null +++ b/docs/reference/blr_prep_ksannotate_y.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_prep_kschart_data.html b/docs/reference/blr_prep_kschart_data.html index 711c1abb..750ac468 100644 --- a/docs/reference/blr_prep_kschart_data.html +++ b/docs/reference/blr_prep_kschart_data.html @@ -1,218 +1,119 @@ - - - - - - +KS Chart data — blr_prep_kschart_data • blorr + Skip to contents -KS Chart data — blr_prep_kschart_data • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    KS Chart data

    - Source: R/blr-plots-data.R - +
    +

    Data for generating KS chart.

    -
    -

    Data for generating KS chart.

    +
    +

    Usage

    + 
    blr_prep_kschart_data(gains_table)
+
+blr_prep_kschart_line(gains_table)
+
+blr_prep_ksannotate_y(ks_line)
+
+blr_prep_kschart_stat(ks_line)
+
+blr_prep_ksannotate_x(ks_line)
    - 
    blr_prep_kschart_data(gains_table)
-
-blr_prep_kschart_line(gains_table)
-
-blr_prep_ksannotate_y(ks_line)
-
-blr_prep_kschart_stat(ks_line)
-
-blr_prep_ksannotate_x(ks_line)
    - -

    Arguments

    - - - - - - - - - - -
    gains_table

    An object of clas blr_gains_table.

    ks_line

    Overall conversion rate.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_prep_kschart_data(gt)
-#>    cum_total_per cum_1s_per cum_0s_per
-#> 1            0.0  0.0000000 0.00000000
-#> 2            0.1  0.2641509 0.04081633
-#> 3            0.2  0.5094340 0.08843537
-#> 4            0.3  0.6981132 0.15646259
-#> 5            0.4  0.8301887 0.24489796
-#> 6            0.5  0.8867925 0.36054422
-#> 7            0.6  0.9433962 0.47619048
-#> 8            0.7  0.9622642 0.60544218
-#> 9            0.8  1.0000000 0.72789116
-#> 10           0.9  1.0000000 0.86394558
-#> 11           1.0  1.0000000 1.00000000
-ks_line <- blr_prep_kschart_line(gt)
-blr_prep_kschart_stat(ks_line)
-#> [1] 59
-blr_prep_ksannotate_y(ks_line)
-#> [1] 0.5375433
-blr_prep_ksannotate_x(ks_line)
-#> [1] 0.5
-
-
    -
    - -
    +
    +

    Arguments

    + + +
    gains_table
    +

    An object of clas blr_gains_table.

    + + +
    ks_line
    +

    Overall conversion rate.

    + +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_prep_kschart_data(gt)
+#>    cum_total_per cum_1s_per cum_0s_per
+#> 1            0.0  0.0000000 0.00000000
+#> 2            0.1  0.2641509 0.04081633
+#> 3            0.2  0.5094340 0.08843537
+#> 4            0.3  0.6981132 0.15646259
+#> 5            0.4  0.8301887 0.24489796
+#> 6            0.5  0.8867925 0.36054422
+#> 7            0.6  0.9433962 0.47619048
+#> 8            0.7  0.9622642 0.60544218
+#> 9            0.8  1.0000000 0.72789116
+#> 10           0.9  1.0000000 0.86394558
+#> 11           1.0  1.0000000 1.00000000
+ks_line <- blr_prep_kschart_line(gt)
+blr_prep_kschart_stat(ks_line)
+#> [1] 59
+blr_prep_ksannotate_y(ks_line)
+#> [1] 0.5375433
+blr_prep_ksannotate_x(ks_line)
+#> [1] 0.5
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_prep_kschart_line.html b/docs/reference/blr_prep_kschart_line.html new file mode 100644 index 00000000..785ddb47 --- /dev/null +++ b/docs/reference/blr_prep_kschart_line.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_prep_kschart_stat.html b/docs/reference/blr_prep_kschart_stat.html new file mode 100644 index 00000000..785ddb47 --- /dev/null +++ b/docs/reference/blr_prep_kschart_stat.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_prep_lchart_data.html b/docs/reference/blr_prep_lchart_data.html new file mode 100644 index 00000000..9595b7b2 --- /dev/null +++ b/docs/reference/blr_prep_lchart_data.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_prep_lchart_gmean.html b/docs/reference/blr_prep_lchart_gmean.html index f1f7b342..975d9208 100644 --- a/docs/reference/blr_prep_lchart_gmean.html +++ b/docs/reference/blr_prep_lchart_gmean.html @@ -1,205 +1,106 @@ - - - - - - +Lift Chart data — blr_prep_lchart_gmean • blorr + Skip to contents -Lift Chart data — blr_prep_lchart_gmean • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Lift Chart data

    - Source: R/blr-plots-data.R - +
    +

    Data for generating lift chart.

    -
    -

    Data for generating lift chart.

    +
    +

    Usage

    + 
    blr_prep_lchart_gmean(gains_table)
+
+blr_prep_lchart_data(gains_table, global_mean)
    - 
    blr_prep_lchart_gmean(gains_table)
-
-blr_prep_lchart_data(gains_table, global_mean)
    - -

    Arguments

    - - - - - - - - - - -
    gains_table

    An object of clas blr_gains_table.

    global_mean

    Overall conversion rate.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-globalmean <- blr_prep_lchart_gmean(gt)
-blr_prep_lchart_data(gt, globalmean)
-#>    decile total  1 decile_mean d_by_g_mean
-#> 1       1    20 14        0.70   2.6415094
-#> 2       2    20 13        0.65   2.4528302
-#> 3       3    20 10        0.50   1.8867925
-#> 4       4    20  7        0.35   1.3207547
-#> 5       5    20  3        0.15   0.5660377
-#> 6       6    20  3        0.15   0.5660377
-#> 7       7    20  1        0.05   0.1886792
-#> 8       8    20  2        0.10   0.3773585
-#> 9       9    20  0        0.00   0.0000000
-#> 10     10    20  0        0.00   0.0000000
-
-
    -
    - -
    +
    +

    Arguments

    + + +
    gains_table
    +

    An object of clas blr_gains_table.

    + + +
    global_mean
    +

    Overall conversion rate.

    + +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+globalmean <- blr_prep_lchart_gmean(gt)
+blr_prep_lchart_data(gt, globalmean)
+#>    decile total  1 decile_mean d_by_g_mean
+#> 1       1    20 14        0.70   2.6415094
+#> 2       2    20 13        0.65   2.4528302
+#> 3       3    20 10        0.50   1.8867925
+#> 4       4    20  7        0.35   1.3207547
+#> 5       5    20  3        0.15   0.5660377
+#> 6       6    20  3        0.15   0.5660377
+#> 7       7    20  1        0.05   0.1886792
+#> 8       8    20  2        0.10   0.3773585
+#> 9       9    20  0        0.00   0.0000000
+#> 10     10    20  0        0.00   0.0000000
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_prep_lorenz_data.html b/docs/reference/blr_prep_lorenz_data.html index b59bb765..75c5339f 100644 --- a/docs/reference/blr_prep_lorenz_data.html +++ b/docs/reference/blr_prep_lorenz_data.html @@ -1,208 +1,109 @@ - - - - - - +Lorenz curve data — blr_prep_lorenz_data • blorr + Skip to contents -Lorenz curve data — blr_prep_lorenz_data • blorr +
    +
    +
    - - +

    Lorenz curve data

    + Source: R/blr-plots-data.R +
    blr_prep_lorenz_data.Rd
    +
    +
    +

    Data for generating Lorenz curve.

    +
    +
    +

    Usage

    + 
    blr_prep_lorenz_data(model, data = NULL, test_data = FALSE)
    +
    +
    +

    Arguments

    - - - - +
    model
    +

    An object of class glm.

    +
    data
    +

    A tibble or data.frame.

    - - -
    -
    - - - - -
    +
    -
    -
    -
    -

    Lorenz curve data

    - Source: R/blr-plots-data.R - +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+data <- model$data
+blr_prep_lorenz_data(model, data, FALSE)
+#>    cum_0s_per cum_1s_per
+#> 1  0.00000000  0.0000000
+#> 2  0.04081633  0.2641509
+#> 3  0.08843537  0.5094340
+#> 4  0.15646259  0.6981132
+#> 5  0.24489796  0.8301887
+#> 6  0.36054422  0.8867925
+#> 7  0.47619048  0.9433962
+#> 8  0.60544218  0.9622642
+#> 9  0.72789116  1.0000000
+#> 10 0.86394558  1.0000000
+#> 11 1.00000000  1.0000000
+#> 12 1.00000000  1.0000000
+
+
    +
    -
    -

    Data for generating Lorenz curve.

    -
    - 
    blr_prep_lorenz_data(model, data = NULL, test_data = FALSE)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    model

    An object of class glm.

    data

    A tibble or data.frame.

    test_data

    Logical; TRUE if data is test data and FALSE if training data.

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-data <- model$data
-blr_prep_lorenz_data(model, data, FALSE)
-#>    cum_0s_per cum_1s_per
-#> 1  0.00000000  0.0000000
-#> 2  0.04081633  0.2641509
-#> 3  0.08843537  0.5094340
-#> 4  0.15646259  0.6981132
-#> 5  0.24489796  0.8301887
-#> 6  0.36054422  0.8867925
-#> 7  0.47619048  0.9433962
-#> 8  0.60544218  0.9622642
-#> 9  0.72789116  1.0000000
-#> 10 0.86394558  1.0000000
-#> 11 1.00000000  1.0000000
-#> 12 1.00000000  1.0000000
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_prep_roc_data.html b/docs/reference/blr_prep_roc_data.html index 9c0caa55..25e1076b 100644 --- a/docs/reference/blr_prep_roc_data.html +++ b/docs/reference/blr_prep_roc_data.html @@ -1,199 +1,100 @@ - - - - - - +ROC curve data — blr_prep_roc_data • blorr + Skip to contents -ROC curve data — blr_prep_roc_data • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    ROC curve data

    - Source: R/blr-plots-data.R - +
    +

    Data for generating ROC curve.

    -
    -

    Data for generating ROC curve.

    +
    +

    Usage

    + 
    blr_prep_roc_data(gains_table)
    - 
    blr_prep_roc_data(gains_table)
    - -

    Arguments

    - - - - - - -
    gains_table

    An object of clas blr_gains_table

    - - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-gt <- blr_gains_table(model)
-blr_prep_roc_data(gt)
-#>    sensitivity specificity sensitivity_per 1 - specificity
-#> 1           NA          NA       0.0000000      0.00000000
-#> 2     26.41509    95.91837       0.2641509      0.04081633
-#> 3     50.94340    91.15646       0.5094340      0.08843537
-#> 4     69.81132    84.35374       0.6981132      0.15646259
-#> 5     83.01887    75.51020       0.8301887      0.24489796
-#> 6     88.67925    63.94558       0.8867925      0.36054422
-#> 7     94.33962    52.38095       0.9433962      0.47619048
-#> 8     96.22642    39.45578       0.9622642      0.60544218
-#> 9    100.00000    27.21088       1.0000000      0.72789116
-#> 10   100.00000    13.60544       1.0000000      0.86394558
-#> 11   100.00000     0.00000       1.0000000      1.00000000
-
-
    -
    - -
    +
    +

    Arguments

    + + +
    gains_table
    +

    An object of clas blr_gains_table

    + +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+gt <- blr_gains_table(model)
+blr_prep_roc_data(gt)
+#>    sensitivity specificity sensitivity_per 1 - specificity
+#> 1           NA          NA       0.0000000      0.00000000
+#> 2     26.41509    95.91837       0.2641509      0.04081633
+#> 3     50.94340    91.15646       0.5094340      0.08843537
+#> 4     69.81132    84.35374       0.6981132      0.15646259
+#> 5     83.01887    75.51020       0.8301887      0.24489796
+#> 6     88.67925    63.94558       0.8867925      0.36054422
+#> 7     94.33962    52.38095       0.9433962      0.47619048
+#> 8     96.22642    39.45578       0.9622642      0.60544218
+#> 9    100.00000    27.21088       1.0000000      0.72789116
+#> 10   100.00000    13.60544       1.0000000      0.86394558
+#> 11   100.00000     0.00000       1.0000000      1.00000000
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_regress.glm.html b/docs/reference/blr_regress.glm.html new file mode 100644 index 00000000..eea1cd8c --- /dev/null +++ b/docs/reference/blr_regress.glm.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_regress.html b/docs/reference/blr_regress.html index e7492878..5d458d9f 100644 --- a/docs/reference/blr_regress.html +++ b/docs/reference/blr_regress.html @@ -1,312 +1,213 @@ - - - - - - +Binary logistic regression — blr_regress • blorr + Skip to contents -Binary logistic regression — blr_regress • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Binary logistic regression

    - Source: R/blr-regress.R - +
    +

    Binary logistic regression.

    -
    -

    Binary logistic regression.

    +
    +

    Usage

    + 
    blr_regress(object, ...)
+
+# S3 method for class 'glm'
+blr_regress(object, odd_conf_limit = FALSE, ...)
    - 
    blr_regress(object, ...)
+    
    
+    
    Arguments
    
 
-# S3 method for glm
-blr_regress(object, odd_conf_limit = FALSE, ...)
    -

    Arguments

    - - - - - - - - - - - - - - -
    object

    An object of class "formula" (or one that can be coerced to +

    object
    +

    An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted or class -glm.

    ...

    Other inputs.

    odd_conf_limit

    If TRUE, odds ratio confidence limts will be displayed.

    +glm.

    + + +
    ...
    +

    Other inputs.

    + + +
    odd_conf_limit
    +

    If TRUE, odds ratio confidence limts will be displayed.

    + +
    + +
    +

    Examples

    + 
    # using formula
+blr_regress(object = honcomp ~ female + read + science, data = hsb2)
+#>                              Model Overview                              
+#> ------------------------------------------------------------------------
+#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
+#> ------------------------------------------------------------------------
+#>   data      honcomp     200        199           196            TRUE     
+#> ------------------------------------------------------------------------
+#> 
+#>                     Response Summary                     
+#> --------------------------------------------------------
+#> Outcome        Frequency        Outcome        Frequency 
+#> --------------------------------------------------------
+#>    0              147              1              53     
+#> --------------------------------------------------------
+#> 
+#>                   Maximum Likelihood Estimates                    
+#> -----------------------------------------------------------------
+#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
+#> -----------------------------------------------------------------
+#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
+#>   female1      1      1.4825        0.4474     3.3139       9e-04 
+#>    read        1      0.1035        0.0258     4.0186       1e-04 
+#>   science      1      0.0948        0.0305     3.1129      0.0019 
+#> -----------------------------------------------------------------
+#> 
+#>  Association of Predicted Probabilities and Observed Responses  
+#> ---------------------------------------------------------------
+#> % Concordant          0.8561          Somers' D        0.7147   
+#> % Discordant          0.1425          Gamma            0.7136   
+#> % Tied                0.0014          Tau-a            0.2794   
+#> Pairs                  7791           c                0.8568   
+#> ---------------------------------------------------------------
+#> 
+
+# using a model built with glm
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+
+blr_regress(model)
+#>                              Model Overview                              
+#> ------------------------------------------------------------------------
+#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
+#> ------------------------------------------------------------------------
+#>   data      honcomp     200        199           196            TRUE     
+#> ------------------------------------------------------------------------
+#> 
+#>                     Response Summary                     
+#> --------------------------------------------------------
+#> Outcome        Frequency        Outcome        Frequency 
+#> --------------------------------------------------------
+#>    0              147              1              53     
+#> --------------------------------------------------------
+#> 
+#>                   Maximum Likelihood Estimates                    
+#> -----------------------------------------------------------------
+#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
+#> -----------------------------------------------------------------
+#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
+#>   female1      1      1.4825        0.4474     3.3139       9e-04 
+#>    read        1      0.1035        0.0258     4.0186       1e-04 
+#>   science      1      0.0948        0.0305     3.1129      0.0019 
+#> -----------------------------------------------------------------
+#> 
+#>  Association of Predicted Probabilities and Observed Responses  
+#> ---------------------------------------------------------------
+#> % Concordant          0.8561          Somers' D        0.7147   
+#> % Discordant          0.1425          Gamma            0.7136   
+#> % Tied                0.0014          Tau-a            0.2794   
+#> Pairs                  7791           c                0.8568   
+#> ---------------------------------------------------------------
+#> 
+
+# odds ratio estimates
+blr_regress(model, odd_conf_limit = TRUE)
+#>                              Model Overview                              
+#> ------------------------------------------------------------------------
+#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
+#> ------------------------------------------------------------------------
+#>   data      honcomp     200        199           196            TRUE     
+#> ------------------------------------------------------------------------
+#> 
+#>                     Response Summary                     
+#> --------------------------------------------------------
+#> Outcome        Frequency        Outcome        Frequency 
+#> --------------------------------------------------------
+#>    0              147              1              53     
+#> --------------------------------------------------------
+#> 
+#>                   Maximum Likelihood Estimates                    
+#> -----------------------------------------------------------------
+#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
+#> -----------------------------------------------------------------
+#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
+#>   female1      1      1.4825        0.4474     3.3139       9e-04 
+#>    read        1      0.1035        0.0258     4.0186       1e-04 
+#>   science      1      0.0948        0.0305     3.1129      0.0019 
+#> -----------------------------------------------------------------
+#> 
+#>                   Odds Ratio Estimates                    
+#> ---------------------------------------------------------
+#>  Effects          Estimate          95% Wald Conf. Limit 
+#> ---------------------------------------------------------
+#>  female1           4.4039          1.8955         11.0521 
+#>    read            1.1091          1.0569          1.1699 
+#>  science           1.0994          1.0377          1.1702 
+#> ---------------------------------------------------------
+#> 
+#>  Association of Predicted Probabilities and Observed Responses  
+#> ---------------------------------------------------------------
+#> % Concordant          0.8561          Somers' D        0.7147   
+#> % Discordant          0.1425          Gamma            0.7136   
+#> % Tied                0.0014          Tau-a            0.2794   
+#> Pairs                  7791           c                0.8568   
+#> ---------------------------------------------------------------
+#> 
+
+
    +
    +
    -

    Examples

    - 
    # using formula
-blr_regress(object = honcomp ~ female + read + science, data = hsb2)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data      honcomp     200        199           196            TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0              147              1              53     
-#> --------------------------------------------------------
-#> 
-#>                   Maximum Likelihood Estimates                    
-#> -----------------------------------------------------------------
-#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
-#> -----------------------------------------------------------------
-#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
-#>   female1      1      1.4825        0.4474     3.3139       9e-04 
-#>    read        1      0.1035        0.0258     4.0186       1e-04 
-#>   science      1      0.0948        0.0305     3.1129      0.0019 
-#> -----------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8561          Somers' D        0.7147   
-#> % Discordant          0.1425          Gamma            0.7136   
-#> % Tied                0.0014          Tau-a            0.2794   
-#> Pairs                  7791           c                0.8568   
-#> ---------------------------------------------------------------
-#> 
-
-# using a model built with glm
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-
-blr_regress(model)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data      honcomp     200        199           196            TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0              147              1              53     
-#> --------------------------------------------------------
-#> 
-#>                   Maximum Likelihood Estimates                    
-#> -----------------------------------------------------------------
-#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
-#> -----------------------------------------------------------------
-#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
-#>   female1      1      1.4825        0.4474     3.3139       9e-04 
-#>    read        1      0.1035        0.0258     4.0186       1e-04 
-#>   science      1      0.0948        0.0305     3.1129      0.0019 
-#> -----------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8561          Somers' D        0.7147   
-#> % Discordant          0.1425          Gamma            0.7136   
-#> % Tied                0.0014          Tau-a            0.2794   
-#> Pairs                  7791           c                0.8568   
-#> ---------------------------------------------------------------
-#> 
-
-# odds ratio estimates
-blr_regress(model, odd_conf_limit = TRUE)
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data      honcomp     200        199           196            TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0              147              1              53     
-#> --------------------------------------------------------
-#> 
-#>                   Maximum Likelihood Estimates                    
-#> -----------------------------------------------------------------
-#>  Parameter     DF    Estimate    Std. Error    z value    Pr(>|z|) 
-#> -----------------------------------------------------------------
-#> (Intercept)    1     -12.7772       1.9755    -6.4677      0.0000 
-#>   female1      1      1.4825        0.4474     3.3139       9e-04 
-#>    read        1      0.1035        0.0258     4.0186       1e-04 
-#>   science      1      0.0948        0.0305     3.1129      0.0019 
-#> -----------------------------------------------------------------
-#> 
-#>                   Odds Ratio Estimates                    
-#> ---------------------------------------------------------
-#>  Effects          Estimate          95% Wald Conf. Limit 
-#> ---------------------------------------------------------
-#>  female1           4.4039          1.8955         11.0521 
-#>    read            1.1091          1.0569          1.1699 
-#>  science           1.0994          1.0377          1.1702 
-#> ---------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8561          Somers' D        0.7147   
-#> % Discordant          0.1425          Gamma            0.7136   
-#> % Tied                0.0014          Tau-a            0.2794   
-#> Pairs                  7791           c                0.8568   
-#> ---------------------------------------------------------------
-#> 
-
-
    -
    - + - - - - + diff --git a/docs/reference/blr_residual_diagnostics.html b/docs/reference/blr_residual_diagnostics.html index 68cb2de4..472b4f6e 100644 --- a/docs/reference/blr_residual_diagnostics.html +++ b/docs/reference/blr_residual_diagnostics.html @@ -1,393 +1,296 @@ - - - - - - +Residual diagnostics — blr_residual_diagnostics • blorr + Skip to contents -Residual diagnostics — blr_residual_diagnostics • blorr +
    -
    -
    -

    Residual diagnostics

    - Source: R/blr-plots.R - +
    + +

    Residual diagnostics

    + Source: R/blr-plots.R +
    blr_residual_diagnostics.Rd
    -
    +

    Diagnostics for confidence interval displacement and detecting ill fitted observations.

    - 
    blr_residual_diagnostics(model)
    +
    +

    Usage

    + 
    blr_residual_diagnostics(model)
    +
    + +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    C, CBAR, DIFDEV and DIFCHISQ.

    +
    -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-blr_residual_diagnostics(model)
-#>                c         cbar      difdev     difchisq
-#> 1   1.714775e-03 1.682909e-03 0.175200955  0.090559751
-#> 2   1.429214e-01 1.394315e-01 3.998937227  5.710209661
-#> 3   9.959732e-04 9.813000e-04 0.130007342  0.066607468
-#> 4   9.023852e-03 8.761106e-03 0.536722606  0.300894248
-#> 5   5.980764e-04 5.917929e-04 0.110220460  0.056328100
-#> 6   2.876007e-03 2.801553e-03 0.208574176  0.108218735
-#> 7   8.995744e-04 8.891766e-04 0.149175691  0.076927702
-#> 8   9.902361e-06 9.877010e-06 0.007711173  0.003858052
-#> 9   1.219551e-02 1.189404e-02 0.800918692  0.481155824
-#> 10  2.163331e-03 2.125096e-03 0.229423218  0.120235791
-#> 11  4.832076e-03 4.727697e-03 0.401357308  0.218861551
-#> 12  6.191650e-02 6.043716e-02 2.609131129  2.529552930
-#> 13  2.995002e-02 2.852142e-02 0.975083831  0.597941207
-#> 14  1.048892e-01 1.034023e-01 4.369886254  7.294114623
-#> 15  2.555955e-05 2.544456e-05 0.011305342  0.005655812
-#> 16  1.890339e-04 1.876253e-04 0.049929085  0.025180243
-#> 17  3.927621e-04 3.891513e-04 0.083321991  0.042330035
-#> 18  4.470660e-03 4.394080e-03 0.461923138  0.256523888
-#> 19  8.222657e-02 7.856862e-02 2.147893802  1.766130095
-#> 20  1.922340e-03 1.894221e-03 0.245647296  0.129496817
-#> 21  3.360228e-02 3.254494e-02 1.465662476  1.034273716
-#> 22  3.070469e-02 2.919663e-02 0.971586410  0.594457116
-#> 23  1.187856e-03 1.172889e-03 0.179275765  0.093083875
-#> 24  1.024315e-02 1.002336e-02 0.779442376  0.467132920
-#> 25  1.666718e-05 1.661460e-05 0.010522075  0.005266506
-#> 26  9.902361e-06 9.877010e-06 0.007711173  0.003858052
-#> 27  3.868987e-02 3.760510e-02 1.754720088  1.341233680
-#> 28  1.397582e-03 1.376421e-03 0.175507244  0.090907237
-#> 29  5.883201e-05 5.851604e-05 0.021732193  0.010895452
-#> 30  1.538551e-03 1.518165e-03 0.218615492  0.114575242
-#> 31  1.206777e-04 1.199131e-04 0.037621469  0.018926550
-#> 32  1.582197e-01 1.510534e-01 3.149359572  3.334983697
-#> 33  2.790231e-02 2.700378e-02 1.254920166  0.838561702
-#> 34  1.832472e-02 1.690403e-02 0.414238978  0.218036836
-#> 35  4.199876e-03 4.115064e-03 0.375701229  0.203778702
-#> 36  4.902223e-02 4.797035e-02 2.417385169  2.235629715
-#> 37  6.184782e-02 5.972581e-02 2.102207404  1.740757786
-#> 38  2.790231e-02 2.700378e-02 1.254920166  0.838561702
-#> 39  7.221982e-05 7.175699e-05 0.022341692  0.011196792
-#> 40  2.371239e-05 2.362595e-05 0.012944686  0.006481368
-#> 41  1.484749e-04 1.473695e-04 0.039352793  0.019794657
-#> 42  3.430254e-03 3.374692e-03 0.382426519  0.208346373
-#> 43  1.538551e-03 1.518165e-03 0.218615492  0.114575242
-#> 44  1.890339e-04 1.876253e-04 0.049929085  0.025180243
-#> 45  3.282110e-02 3.187881e-02 1.538424442  1.110378773
-#> 46  3.133334e-02 2.980554e-02 0.993508264  0.611278393
-#> 47  5.583226e-04 5.524581e-04 0.103098442  0.052595923
-#> 48  2.821714e-02 2.740703e-02 1.378342821  0.954620784
-#> 49  5.980764e-04 5.917929e-04 0.110220460  0.056328100
-#> 50  1.714775e-03 1.682909e-03 0.175200955  0.090559751
-#> 51  1.007565e-01 9.926312e-02 4.216014793  6.697126795
-#> 52  7.587586e-05 7.546277e-05 0.027607615  0.013860744
-#> 53  2.523756e-04 2.503371e-04 0.061302700  0.030993042
-#> 54  1.005310e-02 9.782114e-03 0.631353787  0.362895817
-#> 55  1.049232e-02 1.010920e-02 0.500933885  0.276855002
-#> 56  3.884597e-06 3.876617e-06 0.003774610  0.001887141
-#> 57  7.089019e-06 7.072193e-06 0.005957749  0.002979759
-#> 58  5.314174e-04 5.251688e-04 0.087936379  0.044663202
-#> 59  1.219551e-02 1.189404e-02 0.800918692  0.481155824
-#> 60  2.250623e-04 2.231714e-04 0.052660204  0.026562307
-#> 61  1.937427e-03 1.907634e-03 0.235987514  0.124050481
-#> 62  2.447935e-01 2.312153e-01 3.612373822  4.168455201
-#> 63  3.099590e-05 3.085479e-05 0.013539849  0.006777256
-#> 64  6.322787e-06 6.309041e-06 0.005801949  0.002902014
-#> 65  1.324601e-04 1.313238e-04 0.030518715  0.015308706
-#> 66  2.523756e-04 2.503371e-04 0.061302700  0.030993042
-#> 67  5.521616e-03 5.408598e-03 0.475398199  0.264241258
-#> 68  1.484749e-04 1.473695e-04 0.039352793  0.019794657
-#> 69  2.843505e-02 2.759905e-02 1.362233451  0.938735201
-#> 70  3.258439e-04 3.226311e-04 0.064728136  0.032721649
-#> 71  5.883201e-05 5.851604e-05 0.021732193  0.010895452
-#> 72  7.100426e-03 6.959611e-03 0.610177412  0.350928915
-#> 73  2.223940e-02 2.127829e-02 0.828148303  0.492362786
-#> 74  1.711199e-01 1.636562e-01 3.349721185  3.752139610
-#> 75  3.430254e-03 3.374692e-03 0.382426519  0.208346373
-#> 76  2.115485e-04 2.098617e-04 0.052173679  0.026319642
-#> 77  1.778370e-01 1.751576e-01 5.295761642 11.625223156
-#> 78  4.554600e-02 4.401450e-02 1.736019169  1.308970340
-#> 79  5.923198e-06 5.910812e-06 0.005651206  0.002826627
-#> 80  1.484749e-04 1.473695e-04 0.039352793  0.019794657
-#> 81  1.005310e-02 9.782114e-03 0.631353787  0.362895817
-#> 82  6.867317e-04 6.747790e-04 0.076771905  0.038768758
-#> 83  6.184782e-02 5.972581e-02 2.102207404  1.740757786
-#> 84  3.683128e-02 3.586186e-02 1.770358544  1.362493836
-#> 85  8.739224e-04 8.603094e-04 0.108422252  0.055230101
-#> 86  7.733278e-02 7.545805e-02 2.935899226  3.112646806
-#> 87  1.199785e-03 1.184777e-03 0.182277117  0.094717594
-#> 88  1.568983e-05 1.564199e-05 0.010248659  0.005129580
-#> 89  1.040428e-04 1.031947e-04 0.025264629  0.012659537
-#> 90  5.648486e-03 5.535976e-03 0.497125122  0.277928055
-#> 91  2.296646e-01 2.269371e-01 6.278388092 19.108468289
-#> 92  3.360228e-02 3.254494e-02 1.465662476  1.034273716
-#> 93  2.507119e-03 2.448154e-03 0.200719009  0.104092427
-#> 94  4.620611e-04 4.574798e-04 0.090692860  0.046140576
-#> 95  1.279493e-03 1.263815e-03 0.197703474  0.103142180
-#> 96  5.656904e-02 5.404523e-02 1.663597494  1.211374960
-#> 97  2.249922e-03 2.200995e-03 0.195221725  0.101213040
-#> 98  5.683130e-03 5.505435e-03 0.330657695  0.176077827
-#> 99  1.101009e-04 1.093947e-04 0.033936548  0.017056493
-#> 100 9.143496e-03 8.924746e-03 0.645164254  0.373044051
-#> 101 2.507119e-03 2.448154e-03 0.200719009  0.104092427
-#> 102 7.065249e-02 6.554781e-02 1.382022263  0.907229749
-#> 103 5.496769e-04 5.417171e-04 0.074013559  0.037409012
-#> 104 3.606158e-02 3.480992e-02 1.437638887  1.002911024
-#> 105 2.946148e-02 2.853544e-02 1.331287766  0.907840313
-#> 106 5.676501e-03 5.522539e-03 0.377058254  0.203613395
-#> 107 3.826369e-02 3.727759e-02 1.842392766  1.446484548
-#> 108 9.579393e-04 9.427774e-04 0.116711853  0.059565092
-#> 109 2.558035e-03 2.523557e-03 0.346142203  0.187228311
-#> 110 6.989810e-03 6.772784e-03 0.402548738  0.218132880
-#> 111 3.907660e-02 3.809320e-02 1.897852151  1.513681841
-#> 112 5.546298e-04 5.483299e-04 0.094862141  0.048273447
-#> 113 5.299547e-02 4.993601e-02 1.315205267  0.864984721
-#> 114 7.541574e-03 7.336359e-03 0.486192864  0.269608174
-#> 115 1.021654e-02 9.796266e-03 0.438780016  0.238142198
-#> 116 2.294176e-04 2.275486e-04 0.055328822  0.027930516
-#> 117 4.649203e-03 4.543908e-03 0.370955406  0.200632248
-#> 118 5.889263e-03 5.700167e-03 0.333349523  0.177527665
-#> 119 1.501761e-02 1.469141e-02 1.052883879  0.676357480
-#> 120 3.333480e-04 3.302306e-04 0.069748542  0.035312784
-#> 121 1.926803e-02 1.877147e-02 1.119684321  0.728390444
-#> 122 3.469276e-03 3.422783e-03 0.458987480  0.255407134
-#> 123 1.706084e-03 1.674517e-03 0.175082379  0.090500084
-#> 124 2.023481e-03 1.997373e-03 0.290115157  0.154804754
-#> 125 3.696036e-01 3.642264e-01 6.950796928 25.035516433
-#> 126 1.838113e-02 1.802134e-02 1.330233919  0.920702469
-#> 127 2.330179e-03 2.293319e-03 0.273338950  0.144976356
-#> 128 1.215048e-03 1.197280e-03 0.158677951  0.081875583
-#> 129 2.763419e-03 2.724278e-03 0.354973148  0.192339395
-#> 130 2.783763e-05 2.772063e-05 0.013175835  0.006595622
-#> 131 1.039078e-02 1.019111e-02 0.864222041  0.530342453
-#> 132 2.214178e-02 2.178123e-02 1.729127087  1.337620342
-#> 133 3.552271e-02 3.367987e-02 1.045504661  0.649215871
-#> 134 1.019456e-03 1.007207e-03 0.162085818  0.083830337
-#> 135 2.219997e-02 2.119220e-02 0.793356119  0.466837321
-#> 136 9.896093e-04 9.720041e-04 0.107415754  0.054637523
-#> 137 1.163020e-02 1.130601e-02 0.695134995  0.405593537
-#> 138 1.217273e-03 1.201953e-03 0.183728947  0.095501951
-#> 139 3.521794e-04 3.489574e-04 0.075228440  0.038142864
-#> 140 3.359504e-03 3.297549e-03 0.332778538  0.178807876
-#> 141 1.033954e-04 1.026746e-04 0.029347552  0.014729117
-#> 142 1.626688e-03 1.603332e-03 0.213484010  0.111669095
-#> 143 9.542028e-04 9.406140e-04 0.128917831  0.066049834
-#> 144 1.418413e-03 1.399681e-03 0.203004768  0.105986475
-#> 145 5.989086e-03 5.904450e-03 0.705683222  0.417816515
-#> 146 2.023481e-03 1.997373e-03 0.290115157  0.154804754
-#> 147 4.364463e-02 4.289970e-02 2.574734358  2.513440758
-#> 148 9.098518e-04 8.993024e-04 0.150363722  0.077561516
-#> 149 1.035674e-03 1.021108e-03 0.141267367  0.072604021
-#> 150 4.072810e-04 4.023678e-04 0.066034991  0.033354067
-#> 151 1.213217e-03 1.196667e-03 0.169461679  0.087721966
-#> 152 6.738631e-02 6.568886e-02 2.660468673  2.607750122
-#> 153 1.783288e-03 1.757971e-03 0.235429953  0.123830352
-#> 154 4.078075e-03 4.012484e-03 0.450196915  0.249471329
-#> 155 1.798284e-02 1.763743e-02 1.327136586  0.918244408
-#> 156 9.586315e-02 9.257885e-02 2.750869718  2.702220617
-#> 157 6.920782e-03 6.818132e-03 0.765144454  0.459685870
-#> 158 1.019456e-03 1.007207e-03 0.162085818  0.083830337
-#> 159 5.468997e-04 5.412187e-04 0.102147214  0.052101738
-#> 160 6.956322e-03 6.783894e-03 0.491955607  0.273683670
-#> 161 6.768584e-02 6.659719e-02 3.367956058  4.140599894
-#> 162 1.068424e-02 1.049175e-02 0.931617433  0.582360803
-#> 163 9.161703e-02 8.684071e-02 2.085772458  1.665733364
-#> 164 1.794676e-02 1.749512e-02 1.079052614  0.695190763
-#> 165 8.183569e-03 8.053310e-03 0.829246770  0.505948870
-#> 166 6.112573e-04 6.042976e-04 0.104062462  0.053074637
-#> 167 2.691798e-02 2.597419e-02 1.143807429  0.740814692
-#> 168 7.248779e-04 7.164235e-04 0.119983373  0.061425852
-#> 169 2.032750e-02 1.999351e-02 1.620327724  1.216833025
-#> 170 2.470070e-02 2.429277e-02 1.843798016  1.470963397
-#> 171 1.626688e-03 1.603332e-03 0.213484010  0.111669095
-#> 172 1.832056e-03 1.808523e-03 0.265475697  0.140796573
-#> 173 1.628309e-04 1.613977e-04 0.036505512  0.018336703
-#> 174 5.031675e-02 4.911828e-02 2.308866824  2.062197192
-#> 175 3.718687e-04 3.677289e-04 0.065376651  0.033032492
-#> 176 1.932064e-02 1.881967e-02 1.116748565  0.725807280
-#> 177 1.580237e-02 1.535157e-02 0.881118321  0.538126313
-#> 178 2.184174e-03 2.141404e-03 0.209906333  0.109356853
-#> 179 1.353245e-02 1.320813e-02 0.895192843  0.551116791
-#> 180 3.295260e-02 3.237871e-02 2.147516973  1.859195000
-#> 181 3.826369e-02 3.727759e-02 1.842392766  1.446484548
-#> 182 5.546298e-04 5.483299e-04 0.094862141  0.048273447
-#> 183 4.059106e-02 3.897641e-02 1.420184895  0.979840258
-#> 184 3.989745e-02 3.924613e-02 2.506277005  2.404079826
-#> 185 6.050690e-02 5.680204e-02 1.391329501  0.927678395
-#> 186 1.042797e-02 1.016529e-02 0.690776927  0.403546839
-#> 187 5.989086e-03 5.904450e-03 0.705683222  0.417816515
-#> 188 1.213217e-03 1.196667e-03 0.169461679  0.087721966
-#> 189 1.666544e-02 1.625457e-02 1.034466544  0.659304844
-#> 190 5.036356e-03 4.962806e-03 0.591189342  0.339831218
-#> 191 5.468997e-04 5.412187e-04 0.102147214  0.052101738
-#> 192 8.813239e-04 8.685670e-04 0.117465633  0.060005835
-#> 193 7.150669e-03 7.028620e-03 0.698578733  0.411798254
-#> 194 1.897529e-04 1.881278e-04 0.043648756  0.021966086
-#> 195 7.498085e-02 7.397022e-02 3.841734514  5.488016022
-#> 196 5.676501e-03 5.522539e-03 0.377058254  0.203613395
-#> 197 2.848621e-04 2.821357e-04 0.058393203  0.029477645
-#> 198 1.419097e-02 1.395780e-02 1.248919982  0.849491713
-#> 199 2.255815e-02 2.211660e-02 1.543189126  1.129927516
-#> 200 2.267186e-02 2.204654e-02 1.204840140  0.799328652
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+blr_residual_diagnostics(model)
+#>                c         cbar      difdev     difchisq
+#> 1   1.714775e-03 1.682909e-03 0.175200955  0.090559751
+#> 2   1.429214e-01 1.394315e-01 3.998937227  5.710209661
+#> 3   9.959732e-04 9.813000e-04 0.130007342  0.066607468
+#> 4   9.023852e-03 8.761106e-03 0.536722606  0.300894248
+#> 5   5.980764e-04 5.917929e-04 0.110220460  0.056328100
+#> 6   2.876007e-03 2.801553e-03 0.208574176  0.108218735
+#> 7   8.995744e-04 8.891766e-04 0.149175691  0.076927702
+#> 8   9.902361e-06 9.877010e-06 0.007711173  0.003858052
+#> 9   1.219551e-02 1.189404e-02 0.800918692  0.481155824
+#> 10  2.163331e-03 2.125096e-03 0.229423218  0.120235791
+#> 11  4.832076e-03 4.727697e-03 0.401357308  0.218861551
+#> 12  6.191650e-02 6.043716e-02 2.609131129  2.529552930
+#> 13  2.995002e-02 2.852142e-02 0.975083831  0.597941207
+#> 14  1.048892e-01 1.034023e-01 4.369886254  7.294114623
+#> 15  2.555955e-05 2.544456e-05 0.011305342  0.005655812
+#> 16  1.890339e-04 1.876253e-04 0.049929085  0.025180243
+#> 17  3.927621e-04 3.891513e-04 0.083321991  0.042330035
+#> 18  4.470660e-03 4.394080e-03 0.461923138  0.256523888
+#> 19  8.222657e-02 7.856862e-02 2.147893802  1.766130095
+#> 20  1.922340e-03 1.894221e-03 0.245647296  0.129496817
+#> 21  3.360228e-02 3.254494e-02 1.465662476  1.034273716
+#> 22  3.070469e-02 2.919663e-02 0.971586410  0.594457116
+#> 23  1.187856e-03 1.172889e-03 0.179275765  0.093083875
+#> 24  1.024315e-02 1.002336e-02 0.779442376  0.467132920
+#> 25  1.666718e-05 1.661460e-05 0.010522075  0.005266506
+#> 26  9.902361e-06 9.877010e-06 0.007711173  0.003858052
+#> 27  3.868987e-02 3.760510e-02 1.754720088  1.341233680
+#> 28  1.397582e-03 1.376421e-03 0.175507244  0.090907237
+#> 29  5.883201e-05 5.851604e-05 0.021732193  0.010895452
+#> 30  1.538551e-03 1.518165e-03 0.218615492  0.114575242
+#> 31  1.206777e-04 1.199131e-04 0.037621469  0.018926550
+#> 32  1.582197e-01 1.510534e-01 3.149359572  3.334983697
+#> 33  2.790231e-02 2.700378e-02 1.254920166  0.838561702
+#> 34  1.832472e-02 1.690403e-02 0.414238978  0.218036836
+#> 35  4.199876e-03 4.115064e-03 0.375701229  0.203778702
+#> 36  4.902223e-02 4.797035e-02 2.417385169  2.235629715
+#> 37  6.184782e-02 5.972581e-02 2.102207404  1.740757786
+#> 38  2.790231e-02 2.700378e-02 1.254920166  0.838561702
+#> 39  7.221982e-05 7.175699e-05 0.022341692  0.011196792
+#> 40  2.371239e-05 2.362595e-05 0.012944686  0.006481368
+#> 41  1.484749e-04 1.473695e-04 0.039352793  0.019794657
+#> 42  3.430254e-03 3.374692e-03 0.382426519  0.208346373
+#> 43  1.538551e-03 1.518165e-03 0.218615492  0.114575242
+#> 44  1.890339e-04 1.876253e-04 0.049929085  0.025180243
+#> 45  3.282110e-02 3.187881e-02 1.538424442  1.110378773
+#> 46  3.133334e-02 2.980554e-02 0.993508264  0.611278393
+#> 47  5.583226e-04 5.524581e-04 0.103098442  0.052595923
+#> 48  2.821714e-02 2.740703e-02 1.378342821  0.954620784
+#> 49  5.980764e-04 5.917929e-04 0.110220460  0.056328100
+#> 50  1.714775e-03 1.682909e-03 0.175200955  0.090559751
+#> 51  1.007565e-01 9.926312e-02 4.216014793  6.697126795
+#> 52  7.587586e-05 7.546277e-05 0.027607615  0.013860744
+#> 53  2.523756e-04 2.503371e-04 0.061302700  0.030993042
+#> 54  1.005310e-02 9.782114e-03 0.631353787  0.362895817
+#> 55  1.049232e-02 1.010920e-02 0.500933885  0.276855002
+#> 56  3.884597e-06 3.876617e-06 0.003774610  0.001887141
+#> 57  7.089019e-06 7.072193e-06 0.005957749  0.002979759
+#> 58  5.314174e-04 5.251688e-04 0.087936379  0.044663202
+#> 59  1.219551e-02 1.189404e-02 0.800918692  0.481155824
+#> 60  2.250623e-04 2.231714e-04 0.052660204  0.026562307
+#> 61  1.937427e-03 1.907634e-03 0.235987514  0.124050481
+#> 62  2.447935e-01 2.312153e-01 3.612373822  4.168455201
+#> 63  3.099590e-05 3.085479e-05 0.013539849  0.006777256
+#> 64  6.322787e-06 6.309041e-06 0.005801949  0.002902014
+#> 65  1.324601e-04 1.313238e-04 0.030518715  0.015308706
+#> 66  2.523756e-04 2.503371e-04 0.061302700  0.030993042
+#> 67  5.521616e-03 5.408598e-03 0.475398199  0.264241258
+#> 68  1.484749e-04 1.473695e-04 0.039352793  0.019794657
+#> 69  2.843505e-02 2.759905e-02 1.362233451  0.938735201
+#> 70  3.258439e-04 3.226311e-04 0.064728136  0.032721649
+#> 71  5.883201e-05 5.851604e-05 0.021732193  0.010895452
+#> 72  7.100426e-03 6.959611e-03 0.610177412  0.350928915
+#> 73  2.223940e-02 2.127829e-02 0.828148303  0.492362786
+#> 74  1.711199e-01 1.636562e-01 3.349721185  3.752139610
+#> 75  3.430254e-03 3.374692e-03 0.382426519  0.208346373
+#> 76  2.115485e-04 2.098617e-04 0.052173679  0.026319642
+#> 77  1.778370e-01 1.751576e-01 5.295761642 11.625223156
+#> 78  4.554600e-02 4.401450e-02 1.736019169  1.308970340
+#> 79  5.923198e-06 5.910812e-06 0.005651206  0.002826627
+#> 80  1.484749e-04 1.473695e-04 0.039352793  0.019794657
+#> 81  1.005310e-02 9.782114e-03 0.631353787  0.362895817
+#> 82  6.867317e-04 6.747790e-04 0.076771905  0.038768758
+#> 83  6.184782e-02 5.972581e-02 2.102207404  1.740757786
+#> 84  3.683128e-02 3.586186e-02 1.770358544  1.362493836
+#> 85  8.739224e-04 8.603094e-04 0.108422252  0.055230101
+#> 86  7.733278e-02 7.545805e-02 2.935899226  3.112646806
+#> 87  1.199785e-03 1.184777e-03 0.182277117  0.094717594
+#> 88  1.568983e-05 1.564199e-05 0.010248659  0.005129580
+#> 89  1.040428e-04 1.031947e-04 0.025264629  0.012659537
+#> 90  5.648486e-03 5.535976e-03 0.497125122  0.277928055
+#> 91  2.296646e-01 2.269371e-01 6.278388092 19.108468289
+#> 92  3.360228e-02 3.254494e-02 1.465662476  1.034273716
+#> 93  2.507119e-03 2.448154e-03 0.200719009  0.104092427
+#> 94  4.620611e-04 4.574798e-04 0.090692860  0.046140576
+#> 95  1.279493e-03 1.263815e-03 0.197703474  0.103142180
+#> 96  5.656904e-02 5.404523e-02 1.663597494  1.211374960
+#> 97  2.249922e-03 2.200995e-03 0.195221725  0.101213040
+#> 98  5.683130e-03 5.505435e-03 0.330657695  0.176077827
+#> 99  1.101009e-04 1.093947e-04 0.033936548  0.017056493
+#> 100 9.143496e-03 8.924746e-03 0.645164254  0.373044051
+#> 101 2.507119e-03 2.448154e-03 0.200719009  0.104092427
+#> 102 7.065249e-02 6.554781e-02 1.382022263  0.907229749
+#> 103 5.496769e-04 5.417171e-04 0.074013559  0.037409012
+#> 104 3.606158e-02 3.480992e-02 1.437638887  1.002911024
+#> 105 2.946148e-02 2.853544e-02 1.331287766  0.907840313
+#> 106 5.676501e-03 5.522539e-03 0.377058254  0.203613395
+#> 107 3.826369e-02 3.727759e-02 1.842392766  1.446484548
+#> 108 9.579393e-04 9.427774e-04 0.116711853  0.059565092
+#> 109 2.558035e-03 2.523557e-03 0.346142203  0.187228311
+#> 110 6.989810e-03 6.772784e-03 0.402548738  0.218132880
+#> 111 3.907660e-02 3.809320e-02 1.897852151  1.513681841
+#> 112 5.546298e-04 5.483299e-04 0.094862141  0.048273447
+#> 113 5.299547e-02 4.993601e-02 1.315205267  0.864984721
+#> 114 7.541574e-03 7.336359e-03 0.486192864  0.269608174
+#> 115 1.021654e-02 9.796266e-03 0.438780016  0.238142198
+#> 116 2.294176e-04 2.275486e-04 0.055328822  0.027930516
+#> 117 4.649203e-03 4.543908e-03 0.370955406  0.200632248
+#> 118 5.889263e-03 5.700167e-03 0.333349523  0.177527665
+#> 119 1.501761e-02 1.469141e-02 1.052883879  0.676357480
+#> 120 3.333480e-04 3.302306e-04 0.069748542  0.035312784
+#> 121 1.926803e-02 1.877147e-02 1.119684321  0.728390444
+#> 122 3.469276e-03 3.422783e-03 0.458987480  0.255407134
+#> 123 1.706084e-03 1.674517e-03 0.175082379  0.090500084
+#> 124 2.023481e-03 1.997373e-03 0.290115157  0.154804754
+#> 125 3.696036e-01 3.642264e-01 6.950796928 25.035516433
+#> 126 1.838113e-02 1.802134e-02 1.330233919  0.920702469
+#> 127 2.330179e-03 2.293319e-03 0.273338950  0.144976356
+#> 128 1.215048e-03 1.197280e-03 0.158677951  0.081875583
+#> 129 2.763419e-03 2.724278e-03 0.354973148  0.192339395
+#> 130 2.783763e-05 2.772063e-05 0.013175835  0.006595622
+#> 131 1.039078e-02 1.019111e-02 0.864222041  0.530342453
+#> 132 2.214178e-02 2.178123e-02 1.729127087  1.337620342
+#> 133 3.552271e-02 3.367987e-02 1.045504661  0.649215871
+#> 134 1.019456e-03 1.007207e-03 0.162085818  0.083830337
+#> 135 2.219997e-02 2.119220e-02 0.793356119  0.466837321
+#> 136 9.896093e-04 9.720041e-04 0.107415754  0.054637523
+#> 137 1.163020e-02 1.130601e-02 0.695134995  0.405593537
+#> 138 1.217273e-03 1.201953e-03 0.183728947  0.095501951
+#> 139 3.521794e-04 3.489574e-04 0.075228440  0.038142864
+#> 140 3.359504e-03 3.297549e-03 0.332778538  0.178807876
+#> 141 1.033954e-04 1.026746e-04 0.029347552  0.014729117
+#> 142 1.626688e-03 1.603332e-03 0.213484010  0.111669095
+#> 143 9.542028e-04 9.406140e-04 0.128917831  0.066049834
+#> 144 1.418413e-03 1.399681e-03 0.203004768  0.105986475
+#> 145 5.989086e-03 5.904450e-03 0.705683222  0.417816515
+#> 146 2.023481e-03 1.997373e-03 0.290115157  0.154804754
+#> 147 4.364463e-02 4.289970e-02 2.574734358  2.513440758
+#> 148 9.098518e-04 8.993024e-04 0.150363722  0.077561516
+#> 149 1.035674e-03 1.021108e-03 0.141267367  0.072604021
+#> 150 4.072810e-04 4.023678e-04 0.066034991  0.033354067
+#> 151 1.213217e-03 1.196667e-03 0.169461679  0.087721966
+#> 152 6.738631e-02 6.568886e-02 2.660468673  2.607750122
+#> 153 1.783288e-03 1.757971e-03 0.235429953  0.123830352
+#> 154 4.078075e-03 4.012484e-03 0.450196915  0.249471329
+#> 155 1.798284e-02 1.763743e-02 1.327136586  0.918244408
+#> 156 9.586315e-02 9.257885e-02 2.750869718  2.702220617
+#> 157 6.920782e-03 6.818132e-03 0.765144454  0.459685870
+#> 158 1.019456e-03 1.007207e-03 0.162085818  0.083830337
+#> 159 5.468997e-04 5.412187e-04 0.102147214  0.052101738
+#> 160 6.956322e-03 6.783894e-03 0.491955607  0.273683670
+#> 161 6.768584e-02 6.659719e-02 3.367956058  4.140599894
+#> 162 1.068424e-02 1.049175e-02 0.931617433  0.582360803
+#> 163 9.161703e-02 8.684071e-02 2.085772458  1.665733364
+#> 164 1.794676e-02 1.749512e-02 1.079052614  0.695190763
+#> 165 8.183569e-03 8.053310e-03 0.829246770  0.505948870
+#> 166 6.112573e-04 6.042976e-04 0.104062462  0.053074637
+#> 167 2.691798e-02 2.597419e-02 1.143807429  0.740814692
+#> 168 7.248779e-04 7.164235e-04 0.119983373  0.061425852
+#> 169 2.032750e-02 1.999351e-02 1.620327724  1.216833025
+#> 170 2.470070e-02 2.429277e-02 1.843798016  1.470963397
+#> 171 1.626688e-03 1.603332e-03 0.213484010  0.111669095
+#> 172 1.832056e-03 1.808523e-03 0.265475697  0.140796573
+#> 173 1.628309e-04 1.613977e-04 0.036505512  0.018336703
+#> 174 5.031675e-02 4.911828e-02 2.308866824  2.062197192
+#> 175 3.718687e-04 3.677289e-04 0.065376651  0.033032492
+#> 176 1.932064e-02 1.881967e-02 1.116748565  0.725807280
+#> 177 1.580237e-02 1.535157e-02 0.881118321  0.538126313
+#> 178 2.184174e-03 2.141404e-03 0.209906333  0.109356853
+#> 179 1.353245e-02 1.320813e-02 0.895192843  0.551116791
+#> 180 3.295260e-02 3.237871e-02 2.147516973  1.859195000
+#> 181 3.826369e-02 3.727759e-02 1.842392766  1.446484548
+#> 182 5.546298e-04 5.483299e-04 0.094862141  0.048273447
+#> 183 4.059106e-02 3.897641e-02 1.420184895  0.979840258
+#> 184 3.989745e-02 3.924613e-02 2.506277005  2.404079826
+#> 185 6.050690e-02 5.680204e-02 1.391329501  0.927678395
+#> 186 1.042797e-02 1.016529e-02 0.690776927  0.403546839
+#> 187 5.989086e-03 5.904450e-03 0.705683222  0.417816515
+#> 188 1.213217e-03 1.196667e-03 0.169461679  0.087721966
+#> 189 1.666544e-02 1.625457e-02 1.034466544  0.659304844
+#> 190 5.036356e-03 4.962806e-03 0.591189342  0.339831218
+#> 191 5.468997e-04 5.412187e-04 0.102147214  0.052101738
+#> 192 8.813239e-04 8.685670e-04 0.117465633  0.060005835
+#> 193 7.150669e-03 7.028620e-03 0.698578733  0.411798254
+#> 194 1.897529e-04 1.881278e-04 0.043648756  0.021966086
+#> 195 7.498085e-02 7.397022e-02 3.841734514  5.488016022
+#> 196 5.676501e-03 5.522539e-03 0.377058254  0.203613395
+#> 197 2.848621e-04 2.821357e-04 0.058393203  0.029477645
+#> 198 1.419097e-02 1.395780e-02 1.248919982  0.849491713
+#> 199 2.255815e-02 2.211660e-02 1.543189126  1.129927516
+#> 200 2.267186e-02 2.204654e-02 1.204840140  0.799328652
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_roc_curve-1.png b/docs/reference/blr_roc_curve-1.png index 6d253626..9156c985 100644 Binary files a/docs/reference/blr_roc_curve-1.png and b/docs/reference/blr_roc_curve-1.png differ diff --git a/docs/reference/blr_roc_curve.html b/docs/reference/blr_roc_curve.html index 13edd88b..0a6edfd9 100644 --- a/docs/reference/blr_roc_curve.html +++ b/docs/reference/blr_roc_curve.html @@ -1,261 +1,165 @@ - - - - - - +ROC curve — blr_roc_curve • blorr + Skip to contents -ROC curve — blr_roc_curve • blorr +
    +
    +
    - - - +

    ROC curve

    + Source: R/blr-roc-curve.R +
    blr_roc_curve.Rd
    +
    +
    +

    Receiver operating characteristic curve (ROC) curve is used for assessing + accuracy of the model classification.

    +
    +
    +

    Usage

    + 
    blr_roc_curve(
+  gains_table,
+  title = "ROC Curve",
+  xaxis_title = "1 - Specificity",
+  yaxis_title = "Sensitivity",
+  roc_curve_col = "blue",
+  diag_line_col = "red",
+  point_shape = 18,
+  point_fill = "blue",
+  point_color = "blue",
+  plot_title_justify = 0.5,
+  print_plot = TRUE
+)
    +
    +
    +

    Arguments

    - - +
    gains_table
    +

    An object of class blr_gains_table.

    +
    title
    +

    Plot title.

    - - - - +
    xaxis_title
    +

    X axis title.

    +
    yaxis_title
    +

    Y axis title.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    ROC curve

    - Source: R/blr-roc-curve.R - -
    +
    diag_line_col
    +

    Diagonal line color.

    + + +
    point_shape
    +

    Shape of the points on the roc curve.

    -
    -

    Receiver operating characteristic curve (ROC) curve is used for assessing - accuracy of the model classification.

    -
    - 
    blr_roc_curve(
-  gains_table,
-  title = "ROC Curve",
-  xaxis_title = "1 - Specificity",
-  yaxis_title = "Sensitivity",
-  roc_curve_col = "blue",
-  diag_line_col = "red",
-  point_shape = 18,
-  point_fill = "blue",
-  point_color = "blue",
-  plot_title_justify = 0.5,
-  print_plot = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    gains_table

    An object of class blr_gains_table.

    title

    Plot title.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    roc_curve_col

    Color of the roc curve.

    diag_line_col

    Diagonal line color.

    point_shape

    Shape of the points on the roc curve.

    point_fill

    Fill of the points on the roc curve.

    point_color

    Color of the points on the roc curve.

    plot_title_justify

    Horizontal justification on the plot title.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    References

    +
    point_fill
    +

    Fill of the points on the roc curve.

    + +
    point_color
    +

    Color of the points on the roc curve.

    + + +
    plot_title_justify
    +

    Horizontal justification on the plot title.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    References

    Agresti, A. (2007), An Introduction to Categorical Data Analysis, Second Edition, New York: John Wiley & Sons.

    Hosmer, D. W., Jr. and Lemeshow, S. (2000), Applied Logistic Regression, 2nd Edition, New York: John Wiley & Sons.

    Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.

    Thomas LC, Edelman DB, Crook JN (2002): Credit Scoring and Its Applications. Philadelphia, SIAM Monographs on Mathematical Modeling and Computation.

    -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_test_hosmer_lemeshow()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-k <- blr_gains_table(model)
-blr_roc_curve(k)
-
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_test_hosmer_lemeshow()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+k <- blr_gains_table(model)
+blr_roc_curve(k)
+
+
+
    +
    +
    - + - - - + diff --git a/docs/reference/blr_rsq_adj_count.html b/docs/reference/blr_rsq_adj_count.html index 07605e83..828ea5ea 100644 --- a/docs/reference/blr_rsq_adj_count.html +++ b/docs/reference/blr_rsq_adj_count.html @@ -1,203 +1,106 @@ - - - - - - +Adjusted count R2 — blr_rsq_adj_count • blorr + Skip to contents -Adjusted count R2 — blr_rsq_adj_count • blorr +
    +
    +
    +

    Adjusted count R2

    + Source: R/blr-model-fit-stats.R +
    blr_rsq_adj_count.Rd
    +
    - - - +
    +

    Adjusted count r-squared.

    +
    - +
    +

    Usage

    + 
    blr_rsq_adj_count(model)
    +
    +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - -
    -
    -
    +
    +

    Value

    +

    Adjusted count r-squared.

    - - -
    -
    - - - - - -
    -
    -
    -

    Adjusted count R2

    - Source: R/blr-model-fit-stats.R - +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    -
    -

    Adjusted count r-squared.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_adj_count(model)
+#> [1] 0.2830189
+
+
    +
    - 
    blr_rsq_adj_count(model)
    - -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    - -

    Adjusted count r-squared.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_adj_count(model)
-#> [1] 0.2830189
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_rsq_count.html b/docs/reference/blr_rsq_count.html index 18bd11ca..4f23d0c6 100644 --- a/docs/reference/blr_rsq_count.html +++ b/docs/reference/blr_rsq_count.html @@ -1,191 +1,93 @@ - - - - - - +Count R2 — blr_rsq_count • blorr + Skip to contents -Count R2 — blr_rsq_count • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Count R2

    - Source: R/blr-model-fit-stats.R - +
    +

    Count r-squared.

    -
    -

    Count r-squared.

    +
    +

    Usage

    + 
    blr_rsq_count(model)
    - 
    blr_rsq_count(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    Count r-squared.

    +
    -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_count(model)
-#> [1] 0.81
-
-
    -
    - -
    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_count(model)
+#> [1] 0.81
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_rsq_cox_snell.html b/docs/reference/blr_rsq_cox_snell.html index fc233303..e4aed051 100644 --- a/docs/reference/blr_rsq_cox_snell.html +++ b/docs/reference/blr_rsq_cox_snell.html @@ -1,207 +1,111 @@ - - - - - - +Cox Snell R2 — blr_rsq_cox_snell • blorr + Skip to contents -Cox Snell R2 — blr_rsq_cox_snell • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Cox Snell R2

    - Source: R/blr-model-fit-stats.R - +
    +

    Cox Snell pseudo r-squared.

    -
    -

    Cox Snell pseudo r-squared.

    +
    +

    Usage

    + 
    blr_rsq_cox_snell(model)
    - 
    blr_rsq_cox_snell(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    Cox Snell pseudo r-squared.

    -

    References

    - +
    +
    +

    References

    Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London: Chapman and Hall.

    Maddala, G. S. (1983). Limited dependent and qualitative variables in economics. New York: Cambridge Press.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_cox_snell(model)
-#> [1] 0.2990105
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_cox_snell(model)
+#> [1] 0.2990105
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_rsq_effron.html b/docs/reference/blr_rsq_effron.html index 6df45897..b65d9c80 100644 --- a/docs/reference/blr_rsq_effron.html +++ b/docs/reference/blr_rsq_effron.html @@ -1,207 +1,111 @@ - - - - - - +Effron R2 — blr_rsq_effron • blorr + Skip to contents -Effron R2 — blr_rsq_effron • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Effron R2

    - Source: R/blr-model-fit-stats.R - +
    +

    Effron pseudo r-squared.

    -
    -

    Effron pseudo r-squared.

    +
    +

    Usage

    + 
    blr_rsq_effron(model)
    - 
    blr_rsq_effron(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    Effron pseudo r-squared.

    -

    References

    - -

    Efron, B. (1978). Regression and ANOVA with zero-one data: Measures of residual variation. Journal of +

    +
    +

    References

    +

    Efron, B. (1978). Regression and ANOVA with zero-one data: Measures of residual variation. Journal of the American Statistical Association, 73, 113-121.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_effron(model)
-#> [1] 0.3304513
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_effron(model)
+#> [1] 0.3304513
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_rsq_mcfadden.html b/docs/reference/blr_rsq_mcfadden.html index 18a7f759..8764b64b 100644 --- a/docs/reference/blr_rsq_mcfadden.html +++ b/docs/reference/blr_rsq_mcfadden.html @@ -1,194 +1,97 @@ - - - - - - +McFadden's R2 — blr_rsq_mcfadden • blorr + Skip to contents -McFadden's R2 — blr_rsq_mcfadden • blorr +
    +
    +
    +

    McFadden's R2

    + Source: R/blr-model-fit-stats.R +
    blr_rsq_mcfadden.Rd
    +
    - - - +
    +

    McFadden's pseudo r-squared for the model.

    +
    - +
    +

    Usage

    + 
    blr_rsq_mcfadden(model)
    +
    +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - -
    -
    -
    +
    +

    Value

    +

    McFadden's r-squared.

    - - -
    -
    - - - - - -
    -
    -
    -

    McFadden's R2

    - Source: R/blr-model-fit-stats.R - +
    +

    References

    +

    https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

    -
    -

    McFadden's pseudo r-squared for the model.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_mcfadden(model)
+#> [1] 0.3072023
+
+
    +
    - 
    blr_rsq_mcfadden(model)
    - -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    - -

    McFadden's r-squared.

    -

    References

    - -

    https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_mcfadden(model)
-#> [1] 0.3072023
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_rsq_mcfadden_adj.html b/docs/reference/blr_rsq_mcfadden_adj.html index d5f563a9..7f75960e 100644 --- a/docs/reference/blr_rsq_mcfadden_adj.html +++ b/docs/reference/blr_rsq_mcfadden_adj.html @@ -1,206 +1,110 @@ - - - - - - +McFadden's adjusted R2 — blr_rsq_mcfadden_adj • blorr + Skip to contents -McFadden's adjusted R2 — blr_rsq_mcfadden_adj • blorr +
    +
    +
    +

    McFadden's adjusted R2

    + Source: R/blr-model-fit-stats.R +
    blr_rsq_mcfadden_adj.Rd
    +
    - - - +
    +

    McFadden's adjusted pseudo r-squared for the model.

    +
    - +
    +

    Usage

    + 
    blr_rsq_mcfadden_adj(model)
    +
    +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - -
    -
    -
    +
    +

    Value

    +

    McFadden's adjusted r-squared.

    - - -
    -
    - - - - - -
    -
    -
    -

    McFadden's adjusted R2

    - Source: R/blr-model-fit-stats.R - +
    +

    References

    +

    https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

    - -
    -

    McFadden's adjusted pseudo r-squared for the model.

    +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke(), +blr_test_lr()

    - 
    blr_rsq_mcfadden_adj(model)
    - -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_mcfadden_adj(model)
+#> [1] 0.2726135
+
+
    +
    +
    -

    Value

    -

    McFadden's adjusted r-squared.

    -

    References

    - -

    https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_mcfadden_adj(model)
-#> [1] 0.2726135
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_rsq_mckelvey_zavoina.html b/docs/reference/blr_rsq_mckelvey_zavoina.html index c70a5ab5..1ff0548c 100644 --- a/docs/reference/blr_rsq_mckelvey_zavoina.html +++ b/docs/reference/blr_rsq_mckelvey_zavoina.html @@ -1,207 +1,111 @@ - - - - - - +McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina • blorr + Skip to contents -McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    McKelvey Zavoina R2

    - Source: R/blr-model-fit-stats.R - +
    +

    McKelvey Zavoina pseudo r-squared.

    -
    -

    McKelvey Zavoina pseudo r-squared.

    +
    +

    Usage

    + 
    blr_rsq_mckelvey_zavoina(model)
    - 
    blr_rsq_mckelvey_zavoina(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.

    -

    References

    - -

    McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent +

    +
    +

    References

    +

    McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, 4, 103-12.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_nagelkerke(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_mckelvey_zavoina(model)
-#> [1] 0.5177764
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_nagelkerke(), +blr_test_lr()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_mckelvey_zavoina(model)
+#> [1] 0.5177764
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_rsq_nagelkerke.html b/docs/reference/blr_rsq_nagelkerke.html index 3a872def..340ec4e2 100644 --- a/docs/reference/blr_rsq_nagelkerke.html +++ b/docs/reference/blr_rsq_nagelkerke.html @@ -1,208 +1,112 @@ - - - - - - +Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke • blorr + Skip to contents -Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke • blorr +
    +
    +
    - -
    -
    - - - - -
    - -
    -
    -
    -

    Cragg-Uhler (Nagelkerke) R2

    - Source: R/blr-model-fit-stats.R - +
    +

    Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.

    -
    -

    Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.

    +
    +

    Usage

    + 
    blr_rsq_nagelkerke(model)
    - 
    blr_rsq_nagelkerke(model)
    +
    +

    Arguments

    -

    Arguments

    - - - - - - -
    model

    An object of class glm.

    -

    Value

    +
    model
    +

    An object of class glm.

    +
    +
    +

    Value

    Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.

    -

    References

    - +
    +
    +

    References

    Cragg, S. G., & Uhler, R. (1970). The demand for automobiles. Canadian Journal of Economics, 3, 386-406.

    Maddala, G. S. (1983). Limited dependent and qualitative variables in economics. New York: Cambridge Press.

    Nagelkerke, N. (1991). A note on a general definition of the coefficient of determination.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_test_lr()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_rsq_nagelkerke(model)
-#> [1] 0.4362587
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_test_lr()

    +
    + +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_rsq_nagelkerke(model)
+#> [1] 0.4362587
+
+
    +
    +
    -
    + - - - + diff --git a/docs/reference/blr_segment.default.html b/docs/reference/blr_segment.default.html new file mode 100644 index 00000000..db82135c --- /dev/null +++ b/docs/reference/blr_segment.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_segment.html b/docs/reference/blr_segment.html index d72d6970..80bedcd2 100644 --- a/docs/reference/blr_segment.html +++ b/docs/reference/blr_segment.html @@ -1,215 +1,118 @@ - - - - - - +Event rate — blr_segment • blorr + Skip to contents -Event rate — blr_segment • blorr +
    +
    +
    +

    Event rate

    + Source: R/blr-bivariate-analysis.R +
    blr_segment.Rd
    +
    - - +
    +

    Event rate by segements/levels of a qualitative variable.

    +
    +
    +

    Usage

    + 
    blr_segment(data, response, predictor)
+
+# Default S3 method
+blr_segment(data, response, predictor)
    +
    +
    +

    Arguments

    - - - +
    data
    +

    A tibble or data.frame.

    - +
    response
    +

    Response variable; column in data.

    - +
    predictor
    +

    Predictor variable; column in data.

    - -
    -
    -
    +
    +

    Value

    +

    A tibble.

    - - -
    -
    - - - - - -
    -
    -
    -

    Event rate

    - Source: R/blr-bivariate-analysis.R - +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_bivariate_analysis(), +blr_segment_dist(), +blr_segment_twoway(), +blr_woe_iv(), +blr_woe_iv_stats()

    -
    -

    Event rate by segements/levels of a qualitative variable.

    +
    +

    Examples

    + 
    blr_segment(hsb2, honcomp, prog)
+#> Event By Attributes 
+#> -------------------
+#> pred           1s%  
+#> -------------------
+#>  1             0.04 
+#>  2             0.20 
+#>  3             0.03 
+#> -------------------
+#> 
+
+
    +
    - 
    blr_segment(data, response, predictor)
-
-# S3 method for default
-blr_segment(data, response, predictor)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    A tibble or data.frame.

    response

    Response variable; column in data.

    predictor

    Predictor variable; column in data.

    - -

    Value

    -

    A tibble.

    -

    See also

    - -

    Other bivariate analysis procedures: -blr_bivariate_analysis(), -blr_segment_dist(), -blr_segment_twoway(), -blr_woe_iv_stats(), -blr_woe_iv()

    - -

    Examples

    - 
    blr_segment(hsb2, honcomp, prog)
-#> Event By Attributes 
-#> -------------------
-#> pred           1s%  
-#> -------------------
-#>  1             0.04 
-#>  2             0.20 
-#>  3             0.03 
-#> -------------------
-#> 
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_segment_dist-1.png b/docs/reference/blr_segment_dist-1.png index 6c91b7b6..75d555de 100644 Binary files a/docs/reference/blr_segment_dist-1.png and b/docs/reference/blr_segment_dist-1.png differ diff --git a/docs/reference/blr_segment_dist.html b/docs/reference/blr_segment_dist.html index 5ff31fe3..70428ca3 100644 --- a/docs/reference/blr_segment_dist.html +++ b/docs/reference/blr_segment_dist.html @@ -1,266 +1,169 @@ - - - - - - +Response distribution — blr_segment_dist • blorr + Skip to contents -Response distribution — blr_segment_dist • blorr +
    +
    +
    - - - +

    Response distribution

    + Source: R/blr-bivariate-analysis.R +
    blr_segment_dist.Rd
    +
    +
    +

    Distribution of response variable by segements/levels of a qualitative variable.

    +
    +
    +

    Usage

    + 
    blr_segment_dist(data, response, predictor)
+
+# S3 method for class 'blr_segment_dist'
+plot(
+  x,
+  title = NA,
+  xaxis_title = "Levels",
+  yaxis_title = "Sample Distribution",
+  sec_yaxis_title = "1s Distribution",
+  bar_color = "blue",
+  line_color = "red",
+  print_plot = TRUE,
+  ...
+)
    +
    +
    +

    Arguments

    - - +
    data
    +

    A tibble or a data.frame.

    +
    response
    +

    Response variable; column in data.

    - - - - +
    predictor
    +

    Predictor variable; column in data.

    +
    x
    +

    An object of class blr_segment_dist.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Response distribution

    - Source: R/blr-bivariate-analysis.R - -
    +
    xaxis_title
    +

    X axis title.

    + + +
    yaxis_title
    +

    Y axis title.

    + + +
    sec_yaxis_title
    +

    Secondary y axis title.

    -
    -

    Distribution of response variable by segements/levels of a qualitative variable.

    -
    - 
    blr_segment_dist(data, response, predictor)
-
-# S3 method for blr_segment_dist
-plot(
-  x,
-  title = NA,
-  xaxis_title = "Levels",
-  yaxis_title = "Sample Distribution",
-  sec_yaxis_title = "1s Distribution",
-  bar_color = "blue",
-  line_color = "red",
-  print_plot = TRUE,
-  ...
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data

    A tibble or a data.frame.

    response

    Response variable; column in data.

    predictor

    Predictor variable; column in data.

    x

    An object of class blr_segment_dist.

    title

    Plot title.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    sec_yaxis_title

    Secondary y axis title.

    bar_color

    Bar color.

    line_color

    Line color.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    ...

    Other inputs.

    - -

    Value

    +
    bar_color
    +

    Bar color.

    + +
    line_color
    +

    Line color.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + + +
    ...
    +

    Other inputs.

    + +
    +
    +

    Value

    A tibble.

    -

    See also

    - -

    Other bivariate analysis procedures: -blr_bivariate_analysis(), -blr_segment_twoway(), -blr_segment(), -blr_woe_iv_stats(), -blr_woe_iv()

    - -

    Examples

    - 
    k <- blr_segment_dist(hsb2, honcomp, prog)
-k
-#>          Event Segmentation           
-#> -------------------------------------
-#>   pred       n     1s     n%     1s%  
-#> -------------------------------------
-#>    1        45     7     0.22    0.04 
-#>    2        105    40    0.52    0.20 
-#>    3        50     6     0.25    0.03 
-#> -------------------------------------
-#> 
-
-# plot
-plot(k)
-
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_bivariate_analysis(), +blr_segment(), +blr_segment_twoway(), +blr_woe_iv(), +blr_woe_iv_stats()

    +
    + +
    +

    Examples

    + 
    k <- blr_segment_dist(hsb2, honcomp, prog)
+k
+#>          Event Segmentation           
+#> -------------------------------------
+#>   pred       n     1s     n%     1s%  
+#> -------------------------------------
+#>    1        45     7     0.22    0.04 
+#>    2        105    40    0.52    0.20 
+#>    3        50     6     0.25    0.03 
+#> -------------------------------------
+#> 
+
+# plot
+plot(k)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_segment_twoway.default.html b/docs/reference/blr_segment_twoway.default.html new file mode 100644 index 00000000..dc676281 --- /dev/null +++ b/docs/reference/blr_segment_twoway.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_segment_twoway.html b/docs/reference/blr_segment_twoway.html index 40b8d0c5..fb117df0 100644 --- a/docs/reference/blr_segment_twoway.html +++ b/docs/reference/blr_segment_twoway.html @@ -1,220 +1,123 @@ - - - - - - +Two way event rate — blr_segment_twoway • blorr + Skip to contents -Two way event rate — blr_segment_twoway • blorr +
    +
    +
    +

    Two way event rate

    + Source: R/blr-bivariate-analysis.R +
    blr_segment_twoway.Rd
    +
    +
    +

    Event rate across two qualitative variables.

    +
    +
    +

    Usage

    + 
    blr_segment_twoway(data, response, variable_1, variable_2)
+
+# Default S3 method
+blr_segment_twoway(data, response, variable_1, variable_2)
    +
    - - +
    +

    Arguments

    +
    data
    +

    A tibble or data.frame.

    - - - +
    response
    +

    Response variable; column in data.

    - +
    variable_1
    +

    Column in data.

    - +
    variable_2
    +

    Column in data.

    - -
    -
    -
    +
    +

    Value

    +

    A tibble.

    - - -
    -
    - - - - - -
    -
    -
    -

    Two way event rate

    - Source: R/blr-bivariate-analysis.R - +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_bivariate_analysis(), +blr_segment(), +blr_segment_dist(), +blr_woe_iv(), +blr_woe_iv_stats()

    -
    -

    Event rate across two qualitative variables.

    +
    +

    Examples

    + 
    blr_segment_twoway(hsb2, honcomp, prog, female)
+#>                 prog     
+#> -----------------------
+#> honcomp     0       1     
+#> -----------------------
+#>   1     0.010   0.025   
+#> -----------------------
+#>   2     0.075   0.125   
+#> -----------------------
+#>   3     0.005   0.025   
+#> -----------------------
+
+
    +
    - 
    blr_segment_twoway(data, response, variable_1, variable_2)
-
-# S3 method for default
-blr_segment_twoway(data, response, variable_1, variable_2)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    data

    A tibble or data.frame.

    response

    Response variable; column in data.

    variable_1

    Column in data.

    variable_2

    Column in data.

    - -

    Value

    -

    A tibble.

    -

    See also

    - -

    Other bivariate analysis procedures: -blr_bivariate_analysis(), -blr_segment_dist(), -blr_segment(), -blr_woe_iv_stats(), -blr_woe_iv()

    - -

    Examples

    - 
    blr_segment_twoway(hsb2, honcomp, prog, female)
-#>                 prog     
-#> -----------------------
-#> honcomp     0       1     
-#> -----------------------
-#>   1     0.010   0.025   
-#> -----------------------
-#>   2     0.075   0.125   
-#> -----------------------
-#>   3     0.005   0.025   
-#> -----------------------
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_step_aic_backward.default.html b/docs/reference/blr_step_aic_backward.default.html new file mode 100644 index 00000000..05696538 --- /dev/null +++ b/docs/reference/blr_step_aic_backward.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_step_aic_backward.html b/docs/reference/blr_step_aic_backward.html index a23a1189..2efa43e2 100644 --- a/docs/reference/blr_step_aic_backward.html +++ b/docs/reference/blr_step_aic_backward.html @@ -1,260 +1,179 @@ - - - - - - +Stepwise AIC backward elimination — blr_step_aic_backward • blorr + Skip to contents -Stepwise AIC backward elimination — blr_step_aic_backward • blorr +
    +
    +
    - - - +

    Stepwise AIC backward elimination

    + Source: R/blr-backward-elimination.R +
    blr_step_aic_backward.Rd
    +
    +
    +

    Build regression model from a set of candidate predictor variables by +removing predictors based on akaike information criterion, in a stepwise +manner until there is no variable left to remove any more.

    +
    +
    +

    Usage

    + 
    blr_step_aic_backward(model, ...)
+
+# Default S3 method
+blr_step_aic_backward(model, progress = FALSE, details = FALSE, ...)
+
+# S3 method for class 'blr_step_aic_backward'
+plot(x, text_size = 3, print_plot = TRUE, ...)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm; the model should include all +candidate predictor variables.

    +
    ...
    +

    Other arguments.

    - - - - +
    progress
    +

    Logical; if TRUE, will display variable selection progress.

    +
    details
    +

    Logical; if TRUE, will print the regression result at +each step.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Stepwise AIC backward elimination

    - Source: R/blr-backward-elimination.R - -
    +
    text_size
    +

    size of the text in the plot.

    -
    -

    Build regression model from a set of candidate predictor variables by -removing predictors based on akaike information criterion, in a stepwise -manner until there is no variable left to remove any more.

    -
    - 
    blr_step_aic_backward(model, ...)
-
-# S3 method for default
-blr_step_aic_backward(model, progress = FALSE, details = FALSE, ...)
-
-# S3 method for blr_step_aic_backward
-plot(x, text_size = 3, print_plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm; the model should include all -candidate predictor variables.

    ...

    Other arguments.

    progress

    Logical; if TRUE, will display variable selection progress.

    details

    Logical; if TRUE, will print the regression result at -each step.

    x

    An object of class blr_step_aic_backward.

    text_size

    size of the text in the plot.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    +
    +
    +

    Value

    blr_step_aic_backward returns an object of class "blr_step_aic_backward". An object of class "blr_step_aic_backward" is a list containing the following components:

    -
    model

    model with the least AIC; an object of class glm

    -
    candidates

    candidate predictor variables

    -
    steps

    total number of steps

    -
    predictors

    variables removed from the model

    -
    aics

    akaike information criteria

    -
    bics

    bayesian information criteria

    -
    devs

    deviances

    +
    model
    +

    model with the least AIC; an object of class glm

    + +
    candidates
    +

    candidate predictor variables

    + +
    steps
    +

    total number of steps

    -

    References

    +
    predictors
    +

    variables removed from the model

    +
    aics
    +

    akaike information criteria

    + +
    bics
    +

    bayesian information criteria

    + +
    devs
    +

    deviances

    + +
    +
    +

    References

    Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

    -

    See also

    - -

    Other variable selection procedures: -blr_step_aic_both(), -blr_step_aic_forward(), -blr_step_p_backward(), -blr_step_p_forward()

    - -

    Examples

    - 
    if (FALSE) {
-model <- glm(honcomp ~ female + read + science + math + prog + socst,
-data = hsb2, family = binomial(link = 'logit'))
-
-# elimination summary
-blr_step_aic_backward(model)
-
-# print details of each step
-blr_step_aic_backward(model, details = TRUE)
-
-# plot
-plot(blr_step_aic_backward(model))
-
-# final model
-k <- blr_step_aic_backward(model)
-k$model
-
-}
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other variable selection procedures: +blr_step_aic_both(), +blr_step_aic_forward(), +blr_step_p_backward(), +blr_step_p_forward()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+model <- glm(honcomp ~ female + read + science + math + prog + socst,
+data = hsb2, family = binomial(link = 'logit'))
+
+# elimination summary
+blr_step_aic_backward(model)
+
+# print details of each step
+blr_step_aic_backward(model, details = TRUE)
+
+# plot
+plot(blr_step_aic_backward(model))
+
+# final model
+k <- blr_step_aic_backward(model)
+k$model
+
+} # }
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_step_aic_both.html b/docs/reference/blr_step_aic_both.html index c2ee83d8..b22edd4e 100644 --- a/docs/reference/blr_step_aic_both.html +++ b/docs/reference/blr_step_aic_both.html @@ -1,248 +1,169 @@ - - - - - - - -Stepwise AIC selection — blr_step_aic_both • blorr - - - - - +Stepwise AIC selection — blr_step_aic_both • blorr + Skip to contents - - +
    +
    +
    +

    Stepwise AIC selection

    + Source: R/blr-stepwise-selection.R +
    blr_step_aic_both.Rd
    +
    - - +

    Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criterion, in a -stepwise manner until there is no variable left to enter or remove any more." /> - +stepwise manner until there is no variable left to enter or remove any more.

    +
    +
    +

    Usage

    + 
    blr_step_aic_both(model, details = FALSE, ...)
+
+# S3 method for class 'blr_step_aic_both'
+plot(x, text_size = 3, ...)
    +
    +
    +

    Arguments

    - - - - +
    model
    +

    An object of class lm.

    +
    details
    +

    Logical; if TRUE, details of variable selection will be +printed on screen.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Stepwise AIC selection

    - Source: R/blr-stepwise-selection.R - -
    +
    x
    +

    An object of class blr_step_aic_both.

    -
    -

    Build regression model from a set of candidate predictor variables by -entering and removing predictors based on akaike information criterion, in a -stepwise manner until there is no variable left to enter or remove any more.

    -
    - 
    blr_step_aic_both(model, details = FALSE, ...)
-
-# S3 method for blr_step_aic_both
-plot(x, text_size = 3, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class lm.

    details

    Logical; if TRUE, details of variable selection will be -printed on screen.

    ...

    Other arguments.

    x

    An object of class blr_step_aic_both.

    text_size

    size of the text in the plot.

    - -

    Value

    +
    text_size
    +

    size of the text in the plot.

    +
    +
    +

    Value

    blr_step_aic_both returns an object of class "blr_step_aic_both". An object of class "blr_step_aic_both" is a list containing the following components:

    -
    model

    model with the least AIC; an object of class glm

    -
    candidates

    candidate predictor variables

    -
    predictors

    variables added/removed from the model

    -
    method

    addition/deletion

    -
    aics

    akaike information criteria

    -
    bics

    bayesian information criteria

    -
    devs

    deviances

    -
    steps

    total number of steps

    +
    model
    +

    model with the least AIC; an object of class glm

    + +
    candidates
    +

    candidate predictor variables

    + +
    predictors
    +

    variables added/removed from the model

    + +
    method
    +

    addition/deletion

    + +
    aics
    +

    akaike information criteria

    + +
    bics
    +

    bayesian information criteria

    + +
    devs
    +

    deviances

    -

    References

    +
    steps
    +

    total number of steps

    +
    +
    +

    References

    Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

    -

    See also

    - -

    Other variable selection procedures: -blr_step_aic_backward(), -blr_step_aic_forward(), -blr_step_p_backward(), -blr_step_p_forward()

    - -

    Examples

    - 
    if (FALSE) {
-model <- glm(y ~ ., data = stepwise)
-
-# selection summary
-blr_step_aic_both(model)
-
-# print details at each step
-blr_step_aic_both(model, details = TRUE)
-
-# plot
-plot(blr_step_aic_both(model))
-
-# final model
-k <- blr_step_aic_both(model)
-k$model
-
-}
-
-
    -
    - -
    + +
    +

    See also

    +

    Other variable selection procedures: +blr_step_aic_backward(), +blr_step_aic_forward(), +blr_step_p_backward(), +blr_step_p_forward()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+model <- glm(y ~ ., data = stepwise)
+
+# selection summary
+blr_step_aic_both(model)
+
+# print details at each step
+blr_step_aic_both(model, details = TRUE)
+
+# plot
+plot(blr_step_aic_both(model))
+
+# final model
+k <- blr_step_aic_both(model)
+k$model
+
+} # }
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_step_aic_forward.default.html b/docs/reference/blr_step_aic_forward.default.html new file mode 100644 index 00000000..0827966c --- /dev/null +++ b/docs/reference/blr_step_aic_forward.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_step_aic_forward.html b/docs/reference/blr_step_aic_forward.html index 8e998068..a1bd899a 100644 --- a/docs/reference/blr_step_aic_forward.html +++ b/docs/reference/blr_step_aic_forward.html @@ -1,259 +1,178 @@ - - - - - - +Stepwise AIC forward selection — blr_step_aic_forward • blorr + Skip to contents -Stepwise AIC forward selection — blr_step_aic_forward • blorr +
    +
    +
    - - - +

    Stepwise AIC forward selection

    + Source: R/blr-forward-selection.R +
    blr_step_aic_forward.Rd
    +
    +
    +

    Build regression model from a set of candidate predictor variables by +entering predictors based on chi square statistic, in a stepwise manner +until there is no variable left to enter any more.

    +
    +
    +

    Usage

    + 
    blr_step_aic_forward(model, ...)
+
+# Default S3 method
+blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...)
+
+# S3 method for class 'blr_step_aic_forward'
+plot(x, text_size = 3, print_plot = TRUE, ...)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class glm.

    +
    ...
    +

    Other arguments.

    - - - - +
    progress
    +

    Logical; if TRUE, will display variable selection progress.

    +
    details
    +

    Logical; if TRUE, will print the regression result at +each step.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Stepwise AIC forward selection

    - Source: R/blr-forward-selection.R - -
    +
    text_size
    +

    size of the text in the plot.

    -
    -

    Build regression model from a set of candidate predictor variables by -entering predictors based on chi square statistic, in a stepwise manner -until there is no variable left to enter any more.

    -
    - 
    blr_step_aic_forward(model, ...)
-
-# S3 method for default
-blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...)
-
-# S3 method for blr_step_aic_forward
-plot(x, text_size = 3, print_plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class glm.

    ...

    Other arguments.

    progress

    Logical; if TRUE, will display variable selection progress.

    details

    Logical; if TRUE, will print the regression result at -each step.

    x

    An object of class blr_step_aic_forward.

    text_size

    size of the text in the plot.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    +
    +
    +

    Value

    blr_step_aic_forward returns an object of class "blr_step_aic_forward". An object of class "blr_step_aic_forward" is a list containing the following components:

    -
    model

    model with the least AIC; an object of class glm

    -
    candidates

    candidate predictor variables

    -
    steps

    total number of steps

    -
    predictors

    variables entered into the model

    -
    aics

    akaike information criteria

    -
    bics

    bayesian information criteria

    -
    devs

    deviances

    +
    model
    +

    model with the least AIC; an object of class glm

    + +
    candidates
    +

    candidate predictor variables

    + +
    steps
    +

    total number of steps

    -

    References

    +
    predictors
    +

    variables entered into the model

    +
    aics
    +

    akaike information criteria

    + +
    bics
    +

    bayesian information criteria

    + +
    devs
    +

    deviances

    + +
    +
    +

    References

    Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

    -

    See also

    - -

    Other variable selection procedures: -blr_step_aic_backward(), -blr_step_aic_both(), -blr_step_p_backward(), -blr_step_p_forward()

    - -

    Examples

    - 
    if (FALSE) {
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-family = binomial(link = 'logit'))
-
-# selection summary
-blr_step_aic_forward(model)
-
-# print details of each step
-blr_step_aic_forward(model, details = TRUE)
-
-# plot
-plot(blr_step_aic_forward(model))
-
-# final model
-k <- blr_step_aic_forward(model)
-k$model
-
-}
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other variable selection procedures: +blr_step_aic_backward(), +blr_step_aic_both(), +blr_step_p_backward(), +blr_step_p_forward()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+family = binomial(link = 'logit'))
+
+# selection summary
+blr_step_aic_forward(model)
+
+# print details of each step
+blr_step_aic_forward(model, details = TRUE)
+
+# plot
+plot(blr_step_aic_forward(model))
+
+# final model
+k <- blr_step_aic_forward(model)
+k$model
+
+} # }
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_step_p_backward.default.html b/docs/reference/blr_step_p_backward.default.html new file mode 100644 index 00000000..5c47f796 --- /dev/null +++ b/docs/reference/blr_step_p_backward.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_step_p_backward.html b/docs/reference/blr_step_p_backward.html index 133232ba..6446ac11 100644 --- a/docs/reference/blr_step_p_backward.html +++ b/docs/reference/blr_step_p_backward.html @@ -1,256 +1,175 @@ - - - - - - - -Stepwise backward regression — blr_step_p_backward • blorr - - - - - +Stepwise backward regression — blr_step_p_backward • blorr + Skip to contents - - +
    +
    +
    +

    Stepwise backward regression

    + Source: R/blr-stepwise-backward-regression.R +
    blr_step_p_backward.Rd
    +
    - - +

    Build regression model from a set of candidate predictor variables by removing predictors based on p values, in a stepwise manner until there is -no variable left to remove any more." /> +no variable left to remove any more.

    +
    +
    +

    Usage

    + 
    blr_step_p_backward(model, ...)
+
+# Default S3 method
+blr_step_p_backward(model, prem = 0.3, details = FALSE, ...)
+
+# S3 method for class 'blr_step_p_backward'
+plot(x, model = NA, print_plot = TRUE, ...)
    +
    +
    +

    Arguments

    - - - +
    model
    +

    An object of class lm; the model should include all +candidate predictor variables.

    - +
    ...
    +

    Other inputs.

    - +
    prem
    +

    p value; variables with p more than prem will be removed +from the model.

    - -
    -
    - - - - -
    +
    details
    +

    Logical; if TRUE, will print the regression result at +each step.

    -
    -
    -
    -

    Stepwise backward regression

    - Source: R/blr-stepwise-backward-regression.R - -
    -
    -

    Build regression model from a set of candidate predictor variables by -removing predictors based on p values, in a stepwise manner until there is -no variable left to remove any more.

    -
    +
    x
    +

    An object of class blr_step_p_backward.

    - 
    blr_step_p_backward(model, ...)
-
-# S3 method for default
-blr_step_p_backward(model, prem = 0.3, details = FALSE, ...)
-
-# S3 method for blr_step_p_backward
-plot(x, model = NA, print_plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class lm; the model should include all -candidate predictor variables.

    ...

    Other inputs.

    prem

    p value; variables with p more than prem will be removed -from the model.

    details

    Logical; if TRUE, will print the regression result at -each step.

    x

    An object of class blr_step_p_backward.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    Value

    blr_step_p_backward returns an object of class "blr_step_p_backward". An object of class "blr_step_p_backward" is a list containing the following components:

    -
    model

    model with the least AIC; an object of class glm

    -
    steps

    total number of steps

    -
    removed

    variables removed from the model

    -
    aic

    akaike information criteria

    -
    bic

    bayesian information criteria

    -
    dev

    deviance

    -
    indvar

    predictors

    +
    model
    +

    model with the least AIC; an object of class glm

    + +
    steps
    +

    total number of steps

    + +
    removed
    +

    variables removed from the model

    + +
    aic
    +

    akaike information criteria

    + +
    bic
    +

    bayesian information criteria

    + +
    dev
    +

    deviance

    -

    References

    +
    indvar
    +

    predictors

    +
    +
    +

    References

    Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

    -

    See also

    - -

    Other variable selection procedures: -blr_step_aic_backward(), -blr_step_aic_both(), -blr_step_aic_forward(), -blr_step_p_forward()

    - -

    Examples

    - 
    if (FALSE) {
-# stepwise backward regression
-model <- glm(honcomp ~ female + read + science + math + prog + socst,
-  data = hsb2, family = binomial(link = 'logit'))
-blr_step_p_backward(model)
-
-# stepwise backward regression plot
-model <- glm(honcomp ~ female + read + science + math + prog + socst,
-  data = hsb2, family = binomial(link = 'logit'))
-k <- blr_step_p_backward(model)
-plot(k)
-
-# final model
-k$model
-
-}
-
-
-
    -
    - -
    + +
    +

    See also

    +

    Other variable selection procedures: +blr_step_aic_backward(), +blr_step_aic_both(), +blr_step_aic_forward(), +blr_step_p_forward()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# stepwise backward regression
+model <- glm(honcomp ~ female + read + science + math + prog + socst,
+  data = hsb2, family = binomial(link = 'logit'))
+blr_step_p_backward(model)
+
+# stepwise backward regression plot
+model <- glm(honcomp ~ female + read + science + math + prog + socst,
+  data = hsb2, family = binomial(link = 'logit'))
+k <- blr_step_p_backward(model)
+plot(k)
+
+# final model
+k$model
+
+} # }
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_step_p_both.default.html b/docs/reference/blr_step_p_both.default.html new file mode 100644 index 00000000..a24cbbdb --- /dev/null +++ b/docs/reference/blr_step_p_both.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_step_p_both.html b/docs/reference/blr_step_p_both.html index 3fa57c37..4030c594 100644 --- a/docs/reference/blr_step_p_both.html +++ b/docs/reference/blr_step_p_both.html @@ -1,253 +1,175 @@ - - - - - - +Stepwise regression — blr_step_p_both • blorr + Skip to contents -Stepwise regression — blr_step_p_both • blorr +
    +
    +
    - - - +

    Stepwise regression

    + Source: R/blr-stepwise-regression.R +
    blr_step_p_both.Rd
    +
    +
    +

    Build regression model from a set of candidate predictor variables by +entering and removing predictors based on p values, in a stepwise manner +until there is no variable left to enter or remove any more.

    +
    +
    +

    Usage

    + 
    blr_step_p_both(model, ...)
+
+# Default S3 method
+blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...)
+
+# S3 method for class 'blr_step_p_both'
+plot(x, model = NA, print_plot = TRUE, ...)
    +
    +
    +

    Arguments

    - - +
    model
    +

    An object of class lm; the model should include all +candidate predictor variables.

    +
    ...
    +

    Other arguments.

    - - - - +
    pent
    +

    p value; variables with p value less than pent will enter +into the model.

    +
    prem
    +

    p value; variables with p more than prem will be removed +from the model.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Stepwise regression

    - Source: R/blr-stepwise-regression.R - -
    +
    x
    +

    An object of class blr_step_p_both.

    -
    -

    Build regression model from a set of candidate predictor variables by -entering and removing predictors based on p values, in a stepwise manner -until there is no variable left to enter or remove any more.

    -
    - 
    blr_step_p_both(model, ...)
-
-# S3 method for default
-blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...)
-
-# S3 method for blr_step_p_both
-plot(x, model = NA, print_plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class lm; the model should include all -candidate predictor variables.

    ...

    Other arguments.

    pent

    p value; variables with p value less than pent will enter -into the model.

    prem

    p value; variables with p more than prem will be removed -from the model.

    details

    Logical; if TRUE, will print the regression result at -each step.

    x

    An object of class blr_step_p_both.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    +
    +
    +

    Value

    blr_step_p_both returns an object of class "blr_step_p_both". An object of class "blr_step_p_both" is a list containing the following components:

    -
    model

    final model; an object of class glm

    -
    orders

    candidate predictor variables according to the order by which they were added or removed from the model

    -
    method

    addition/deletion

    -
    steps

    total number of steps

    -
    predictors

    variables retained in the model (after addition)

    -
    aic

    akaike information criteria

    -
    bic

    bayesian information criteria

    -
    dev

    deviance

    -
    indvar

    predictors

    - -

    References

    +
    model
    +

    final model; an object of class glm

    + +
    orders
    +

    candidate predictor variables according to the order by which they were added or removed from the model

    + +
    method
    +

    addition/deletion

    + +
    steps
    +

    total number of steps

    + +
    predictors
    +

    variables retained in the model (after addition)

    + +
    aic
    +

    akaike information criteria

    + +
    bic
    +

    bayesian information criteria

    +
    dev
    +

    deviance

    + +
    indvar
    +

    predictors

    + +
    +
    +

    References

    Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

    +
    -

    Examples

    - 
    if (FALSE) {
-# stepwise regression
-model <- glm(y ~ ., data = stepwise)
-blr_step_p_both(model)
-
-# stepwise regression plot
-model <- glm(y ~ ., data = stepwise)
-k <- blr_step_p_both(model)
-plot(k)
-
-# final model
-k$model
-
-}
-
-
    -
    - -
    +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# stepwise regression
+model <- glm(y ~ ., data = stepwise)
+blr_step_p_both(model)
+
+# stepwise regression plot
+model <- glm(y ~ ., data = stepwise)
+k <- blr_step_p_both(model)
+plot(k)
+
+# final model
+k$model
+
+} # }
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_step_p_forward.default.html b/docs/reference/blr_step_p_forward.default.html new file mode 100644 index 00000000..65dfb716 --- /dev/null +++ b/docs/reference/blr_step_p_forward.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_step_p_forward.html b/docs/reference/blr_step_p_forward.html index 21ad5d77..0b53df05 100644 --- a/docs/reference/blr_step_p_forward.html +++ b/docs/reference/blr_step_p_forward.html @@ -1,257 +1,176 @@ - - - - - - - -Stepwise forward regression — blr_step_p_forward • blorr - - - - - +Stepwise forward regression — blr_step_p_forward • blorr + Skip to contents - - +
    +
    +
    +

    Stepwise forward regression

    + Source: R/blr-stepwise-forward-regression.R +
    blr_step_p_forward.Rd
    +
    - - +

    Build regression model from a set of candidate predictor variables by entering predictors based on p values, in a stepwise manner until there is -no variable left to enter any more." /> +no variable left to enter any more.

    +
    +
    +

    Usage

    + 
    blr_step_p_forward(model, ...)
+
+# Default S3 method
+blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)
+
+# S3 method for class 'blr_step_p_forward'
+plot(x, model = NA, print_plot = TRUE, ...)
    +
    +
    +

    Arguments

    - - - +
    model
    +

    An object of class lm; the model should include all +candidate predictor variables.

    - +
    ...
    +

    Other arguments.

    - +
    penter
    +

    p value; variables with p value less than penter will +enter into the model

    - -
    -
    - - - - -
    +
    details
    +

    Logical; if TRUE, will print the regression result at +each step.

    -
    -
    -
    -

    Stepwise forward regression

    - Source: R/blr-stepwise-forward-regression.R - -
    -
    -

    Build regression model from a set of candidate predictor variables by -entering predictors based on p values, in a stepwise manner until there is -no variable left to enter any more.

    -
    +
    x
    +

    An object of class blr_step_p_forward.

    - 
    blr_step_p_forward(model, ...)
-
-# S3 method for default
-blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)
-
-# S3 method for blr_step_p_forward
-plot(x, model = NA, print_plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model

    An object of class lm; the model should include all -candidate predictor variables.

    ...

    Other arguments.

    penter

    p value; variables with p value less than penter will -enter into the model

    details

    Logical; if TRUE, will print the regression result at -each step.

    x

    An object of class blr_step_p_forward.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    Value

    blr_step_p_forward returns an object of class "blr_step_p_forward". An object of class "blr_step_p_forward" is a list containing the following components:

    -
    model

    model with the least AIC; an object of class glm

    -
    steps

    number of steps

    -
    predictors

    variables added to the model

    -
    aic

    akaike information criteria

    -
    bic

    bayesian information criteria

    -
    dev

    deviance

    -
    indvar

    predictors

    +
    model
    +

    model with the least AIC; an object of class glm

    + +
    steps
    +

    number of steps

    + +
    predictors
    +

    variables added to the model

    + +
    aic
    +

    akaike information criteria

    + +
    bic
    +

    bayesian information criteria

    + +
    dev
    +

    deviance

    -

    References

    +
    indvar
    +

    predictors

    +
    +
    +

    References

    Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

    Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

    -

    See also

    - -

    Other variable selection procedures: -blr_step_aic_backward(), -blr_step_aic_both(), -blr_step_aic_forward(), -blr_step_p_backward()

    - -

    Examples

    - 
    if (FALSE) {
-# stepwise forward regression
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-  family = binomial(link = 'logit'))
-blr_step_p_forward(model)
-
-# stepwise forward regression plot
-model <- glm(honcomp ~ female + read + science, data = hsb2,
-  family = binomial(link = 'logit'))
-k <- blr_step_p_forward(model)
-plot(k)
-
-# final model
-k$model
-
-}
-
-
    -
    - -
    + +
    +

    See also

    +

    Other variable selection procedures: +blr_step_aic_backward(), +blr_step_aic_both(), +blr_step_aic_forward(), +blr_step_p_backward()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# stepwise forward regression
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+  family = binomial(link = 'logit'))
+blr_step_p_forward(model)
+
+# stepwise forward regression plot
+model <- glm(honcomp ~ female + read + science, data = hsb2,
+  family = binomial(link = 'logit'))
+k <- blr_step_p_forward(model)
+plot(k)
+
+# final model
+k$model
+
+} # }
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_test_hosmer_lemeshow.html b/docs/reference/blr_test_hosmer_lemeshow.html index 4af7dbf7..7a11ae0e 100644 --- a/docs/reference/blr_test_hosmer_lemeshow.html +++ b/docs/reference/blr_test_hosmer_lemeshow.html @@ -1,229 +1,132 @@ - - - - - - +Hosmer lemeshow test — blr_test_hosmer_lemeshow • blorr + Skip to contents -Hosmer lemeshow test — blr_test_hosmer_lemeshow • blorr +
    +
    +
    +

    Hosmer lemeshow test

    + Source: R/blr-hosmer-lemeshow-test.R +
    blr_test_hosmer_lemeshow.Rd
    +
    +
    +

    Hosmer lemeshow goodness of fit test.

    +
    +
    +

    Usage

    + 
    blr_test_hosmer_lemeshow(model, data = NULL)
    +
    - - - +
    +

    Arguments

    - +
    model
    +

    An object of class glm.

    - +
    data
    +

    a tibble or data.frame.

    - -
    -
    -
    +
    +

    References

    +

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). +New York: John Wiley & Sons.

    - - -
    -
    - - - - - -
    -
    -
    -

    Hosmer lemeshow test

    - Source: R/blr-hosmer-lemeshow-test.R - +
    +

    See also

    +

    Other model validation techniques: +blr_confusion_matrix(), +blr_decile_capture_rate(), +blr_decile_lift_chart(), +blr_gains_table(), +blr_gini_index(), +blr_ks_chart(), +blr_lorenz_curve(), +blr_roc_curve()

    -
    -

    Hosmer lemeshow goodness of fit test.

    +
    +

    Examples

    + 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
+             family = binomial(link = 'logit'))
+
+blr_test_hosmer_lemeshow(model)
+#>            Partition for the Hosmer & Lemeshow Test            
+#> --------------------------------------------------------------
+#>                         def = 1                 def = 0        
+#> Group    Total    Observed    Expected    Observed    Expected 
+#> --------------------------------------------------------------
+#>   1       20         0          0.16         20        19.84   
+#>   2       20         0          0.53         20        19.47   
+#>   3       20         2          0.99         18        19.01   
+#>   4       20         1          1.64         19        18.36   
+#>   5       21         3          2.72         18        18.28   
+#>   6       19         3          4.05         16        14.95   
+#>   7       20         7          6.50         13        13.50   
+#>   8       20         10         8.90         10        11.10   
+#>   9       20         13        11.49         7          8.51   
+#>  10       20         14        16.02         6          3.98   
+#> --------------------------------------------------------------
+#> 
+#>      Goodness of Fit Test      
+#> ------------------------------
+#> Chi-Square    DF    Pr > ChiSq 
+#> ------------------------------
+#>   4.4998      8       0.8095   
+#> ------------------------------
+
+
    +
    - 
    blr_test_hosmer_lemeshow(model, data = NULL)
    - -

    Arguments

    - - - - - - - - - - -
    model

    An object of class glm.

    data

    a tibble or data.frame.

    - -

    References

    -

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). -New York: John Wiley & Sons.

    -

    See also

    - -

    Other model validation techniques: -blr_confusion_matrix(), -blr_decile_capture_rate(), -blr_decile_lift_chart(), -blr_gains_table(), -blr_gini_index(), -blr_ks_chart(), -blr_lorenz_curve(), -blr_roc_curve()

    - -

    Examples

    - 
    model <- glm(honcomp ~ female + read + science, data = hsb2,
-             family = binomial(link = 'logit'))
-
-blr_test_hosmer_lemeshow(model)
-#>            Partition for the Hosmer & Lemeshow Test            
-#> --------------------------------------------------------------
-#>                         def = 1                 def = 0        
-#> Group    Total    Observed    Expected    Observed    Expected 
-#> --------------------------------------------------------------
-#>   1       20         0          0.16         20        19.84   
-#>   2       20         0          0.53         20        19.47   
-#>   3       20         2          0.99         18        19.01   
-#>   4       20         1          1.64         19        18.36   
-#>   5       21         3          2.72         18        18.28   
-#>   6       19         3          4.05         16        14.95   
-#>   7       20         7          6.50         13        13.50   
-#>   8       20         10         8.90         10        11.10   
-#>   9       20         13        11.49         7          8.51   
-#>  10       20         14        16.02         6          3.98   
-#> --------------------------------------------------------------
-#> 
-#>      Goodness of Fit Test      
-#> ------------------------------
-#> Chi-Square    DF    Pr > ChiSq 
-#> ------------------------------
-#>   4.4998      8       0.8095   
-#> ------------------------------
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_test_lr.default.html b/docs/reference/blr_test_lr.default.html new file mode 100644 index 00000000..8a3eeb09 --- /dev/null +++ b/docs/reference/blr_test_lr.default.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_test_lr.html b/docs/reference/blr_test_lr.html index 6405fd92..67152cf7 100644 --- a/docs/reference/blr_test_lr.html +++ b/docs/reference/blr_test_lr.html @@ -1,220 +1,123 @@ - - - - - - +Likelihood ratio test — blr_test_lr • blorr + Skip to contents -Likelihood ratio test — blr_test_lr • blorr +
    +
    +
    +

    Likelihood ratio test

    + Source: R/blr-lrtest.R +
    blr_test_lr.Rd
    +
    +
    +

    Performs the likelihood ratio test for full and reduced model.

    +
    +
    +

    Usage

    + 
    blr_test_lr(full_model, reduced_model)
+
+# Default S3 method
+blr_test_lr(full_model, reduced_model)
    +
    - - - +
    +

    Arguments

    - +
    full_model
    +

    An object of class glm; model with all predictors.

    - +
    reduced_model
    +

    An object of class glm; nested model. Optional if +you are comparing the full_model with an intercept only model.

    - -
    -
    -
    +
    +

    Value

    +

    Two tibbles with model information and test results.

    - - -
    -
    - - - - - -
    -
    -
    -

    Likelihood ratio test

    - Source: R/blr-lrtest.R - +
    +

    See also

    +

    Other model fit statistics: +blr_model_fit_stats(), +blr_multi_model_fit_stats(), +blr_pairs(), +blr_rsq_adj_count(), +blr_rsq_cox_snell(), +blr_rsq_effron(), +blr_rsq_mcfadden_adj(), +blr_rsq_mckelvey_zavoina(), +blr_rsq_nagelkerke()

    -
    -

    Performs the likelihood ratio test for full and reduced model.

    +
    +

    Examples

    + 
    # compare full model with intercept only model
+# full model
+model_1 <- glm(honcomp ~ female + read + science, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_test_lr(model_1)
+
+# compare full model with nested model
+# nested model
+model_2 <- glm(honcomp ~ female + read, data = hsb2,
+            family = binomial(link = 'logit'))
+
+blr_test_lr(model_1, model_2)
+
+
+
    +
    - 
    blr_test_lr(full_model, reduced_model)
-
-# S3 method for default
-blr_test_lr(full_model, reduced_model)
    - -

    Arguments

    - - - - - - - - - - -
    full_model

    An object of class glm; model with all predictors.

    reduced_model

    An object of class glm; nested model. Optional if -you are comparing the full_model with an intercept only model.

    -

    Value

    - -

    Two tibbles with model information and test results.

    -

    See also

    - -

    Other model fit statistics: -blr_model_fit_stats(), -blr_multi_model_fit_stats(), -blr_pairs(), -blr_rsq_adj_count(), -blr_rsq_cox_snell(), -blr_rsq_effron(), -blr_rsq_mcfadden_adj(), -blr_rsq_mckelvey_zavoina(), -blr_rsq_nagelkerke()

    - -

    Examples

    - 
    # compare full model with intercept only model
-# full model
-model_1 <- glm(honcomp ~ female + read + science, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_test_lr(model_1)
-
-# compare full model with nested model
-# nested model
-model_2 <- glm(honcomp ~ female + read, data = hsb2,
-            family = binomial(link = 'logit'))
-
-blr_test_lr(model_1, model_2)
-
-
-
    -
    - +
    - -
    - - - + diff --git a/docs/reference/blr_vif_tol.html b/docs/reference/blr_vif_tol.html new file mode 100644 index 00000000..2ae717b1 --- /dev/null +++ b/docs/reference/blr_vif_tol.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/blr_woe_iv-1.png b/docs/reference/blr_woe_iv-1.png index 3afa1a65..d63fb4bc 100644 Binary files a/docs/reference/blr_woe_iv-1.png and b/docs/reference/blr_woe_iv-1.png differ diff --git a/docs/reference/blr_woe_iv.html b/docs/reference/blr_woe_iv.html index dd27158d..5313696d 100644 --- a/docs/reference/blr_woe_iv.html +++ b/docs/reference/blr_woe_iv.html @@ -1,277 +1,182 @@ - - - - - - +WoE & IV — blr_woe_iv • blorr + Skip to contents -WoE & IV — blr_woe_iv • blorr +
    +
    +
    - - - +

    WoE & IV

    + Source: R/blr-woe-iv.R +
    blr_woe_iv.Rd
    +
    +
    +

    Weight of evidence and information value. Currently avialable for categorical + predictors only.

    +
    +
    +

    Usage

    + 
    blr_woe_iv(data, predictor, response, digits = 4, ...)
+
+# S3 method for class 'blr_woe_iv'
+plot(
+  x,
+  title = NA,
+  xaxis_title = "Levels",
+  yaxis_title = "WoE",
+  bar_color = "blue",
+  line_color = "red",
+  print_plot = TRUE,
+  ...
+)
    +
    +
    +

    Arguments

    - - +
    data
    +

    A tibble or data.frame.

    +
    predictor
    +

    Predictor variable; column in data.

    - - - - +
    response
    +

    Response variable; column in data.

    +
    digits
    +

    Number of decimal digits to round off.

    - - -
    -
    - - - - -
    -
    -
    -
    -

    WoE & IV

    - Source: R/blr-woe-iv.R - -
    +
    x
    +

    An object of class blr_segment_dist.

    -
    -

    Weight of evidence and information value. Currently avialable for categorical - predictors only.

    -
    - 
    blr_woe_iv(data, predictor, response, digits = 4, ...)
-
-# S3 method for blr_woe_iv
-plot(
-  x,
-  title = NA,
-  xaxis_title = "Levels",
-  yaxis_title = "WoE",
-  bar_color = "blue",
-  line_color = "red",
-  print_plot = TRUE,
-  ...
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data

    A tibble or data.frame.

    predictor

    Predictor variable; column in data.

    response

    Response variable; column in data.

    digits

    Number of decimal digits to round off.

    ...

    Other inputs.

    x

    An object of class blr_segment_dist.

    title

    Plot title.

    xaxis_title

    X axis title.

    yaxis_title

    Y axis title.

    bar_color

    Color of the bar.

    line_color

    Color of the horizontal line.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - -

    Value

    +
    title
    +

    Plot title.

    -

    A tibble.

    -

    References

    +
    xaxis_title
    +

    X axis title.

    + + +
    yaxis_title
    +

    Y axis title.

    + + +
    bar_color
    +

    Color of the bar.

    + + +
    line_color
    +

    Color of the horizontal line.

    + + +
    print_plot
    +

    logical; if TRUE, prints the plot else returns a plot object.

    + +
    +
    +

    Value

    +

    A tibble.

    +
    +
    +

    References

    Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.

    -

    See also

    - -

    Other bivariate analysis procedures: -blr_bivariate_analysis(), -blr_segment_dist(), -blr_segment_twoway(), -blr_segment(), -blr_woe_iv_stats()

    - -

    Examples

    - 
    # woe and iv
-k <- blr_woe_iv(hsb2, female, honcomp)
-k
-#>                            Weight of Evidence                             
-#> -------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> -------------------------------------------------------------------------
-#>   0          73          18          0.50       0.34       0.38     0.06  
-#>   1          74          35          0.50       0.66      -0.27     0.04  
-#> -------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>  female          0.1023       
-#> -----------------------------
-
-# plot woe
-plot(k)
-
-
-
    -
    - -
    +
    +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_bivariate_analysis(), +blr_segment(), +blr_segment_dist(), +blr_segment_twoway(), +blr_woe_iv_stats()

    +
    + +
    +

    Examples

    + 
    # woe and iv
+k <- blr_woe_iv(hsb2, female, honcomp)
+k
+#>                            Weight of Evidence                             
+#> -------------------------------------------------------------------------
+#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> -------------------------------------------------------------------------
+#>   0          73          18          0.50       0.34       0.38     0.06  
+#>   1          74          35          0.50       0.66      -0.27     0.04  
+#> -------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>  female          0.1023       
+#> -----------------------------
+
+# plot woe
+plot(k)
+
+
+
    +
    + - + - - - + diff --git a/docs/reference/blr_woe_iv_stats.html b/docs/reference/blr_woe_iv_stats.html index 4b559ab1..e8b22f45 100644 --- a/docs/reference/blr_woe_iv_stats.html +++ b/docs/reference/blr_woe_iv_stats.html @@ -1,277 +1,180 @@ - - - - - - +Multi variable WOE & IV — blr_woe_iv_stats • blorr + Skip to contents -Multi variable WOE & IV — blr_woe_iv_stats • blorr +
    +
    +
    +

    Multi variable WOE & IV

    + Source: R/blr-woe-iv.R +
    blr_woe_iv_stats.Rd
    +
    - - - +
    +

    Prints weight of evidence and information value for multiple variables. + Currently avialable for categorical predictors only.

    +
    - +
    +

    Usage

    + 
    blr_woe_iv_stats(data, response, ...)
    +
    +
    +

    Arguments

    - +
    data
    +

    A data.frame or tibble.

    - -
    -
    - +
    response
    +

    Response variable; column in data.

    - -
    +
    ...
    +

    Predictor variables; column in data.

    -
    -
    -
    -

    Multi variable WOE & IV

    - Source: R/blr-woe-iv.R - +
    +
    +

    See also

    +

    Other bivariate analysis procedures: +blr_bivariate_analysis(), +blr_segment(), +blr_segment_dist(), +blr_segment_twoway(), +blr_woe_iv()

    -
    -

    Prints weight of evidence and information value for multiple variables. - Currently avialable for categorical predictors only.

    +
    +

    Examples

    + 
    blr_woe_iv_stats(hsb2, honcomp, prog, race, female, schtyp)
+#> Variable: prog
+#> 
+#>                            Weight of Evidence                             
+#> -------------------------------------------------------------------------
+#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> -------------------------------------------------------------------------
+#>   1          38          7           0.26       0.13       0.67     0.08  
+#>   2          65          40          0.44       0.75      -0.53     0.17  
+#>   3          44          6           0.30       0.11       0.97     0.18  
+#> -------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>   prog           0.4329       
+#> -----------------------------
+#> 
+#> 
+#> Variable: race
+#> 
+#>                            Weight of Evidence                             
+#> -------------------------------------------------------------------------
+#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> -------------------------------------------------------------------------
+#>   1          22          2           0.15       0.04       1.38     0.15  
+#>   2          6           5           0.04       0.09      -0.84     0.04  
+#>   3          18          2           0.12       0.04       1.18     0.10  
+#>   4         101          44          0.69       0.83      -0.19     0.03  
+#> -------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>   race            0.326       
+#> -----------------------------
+#> 
+#> 
+#> Variable: female
+#> 
+#>                            Weight of Evidence                             
+#> -------------------------------------------------------------------------
+#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> -------------------------------------------------------------------------
+#>   0          73          18          0.50       0.34       0.38     0.06  
+#>   1          74          35          0.50       0.66      -0.27     0.04  
+#> -------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>  female          0.1023       
+#> -----------------------------
+#> 
+#> 
+#> Variable: schtyp
+#> 
+#>                            Weight of Evidence                            
+#> ------------------------------------------------------------------------
+#> levels    count_0s    count_1s    dist_0s    dist_1s        woe     iv   
+#> ------------------------------------------------------------------------
+#>   1         123          45          0.84       0.85      -0.01    0.00  
+#>   2          24          8           0.16       0.15       0.08    0.00  
+#> ------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>  schtyp          0.0012       
+#> -----------------------------
+#> 
+#> 
+
+
    +
    - 
    blr_woe_iv_stats(data, response, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    A data.frame or tibble.

    response

    Response variable; column in data.

    ...

    Predictor variables; column in data.

    -

    See also

    - -

    Other bivariate analysis procedures: -blr_bivariate_analysis(), -blr_segment_dist(), -blr_segment_twoway(), -blr_segment(), -blr_woe_iv()

    - -

    Examples

    - 
    blr_woe_iv_stats(hsb2, honcomp, prog, race, female, schtyp)
-#> Variable: prog
-#> 
-#>                            Weight of Evidence                             
-#> -------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> -------------------------------------------------------------------------
-#>   1          38          7           0.26       0.13       0.67     0.08  
-#>   2          65          40          0.44       0.75      -0.53     0.17  
-#>   3          44          6           0.30       0.11       0.97     0.18  
-#> -------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   prog           0.4329       
-#> -----------------------------
-#> 
-#> 
-#> Variable: race
-#> 
-#>                            Weight of Evidence                             
-#> -------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> -------------------------------------------------------------------------
-#>   1          22          2           0.15       0.04       1.38     0.15  
-#>   2          6           5           0.04       0.09      -0.84     0.04  
-#>   3          18          2           0.12       0.04       1.18     0.10  
-#>   4         101          44          0.69       0.83      -0.19     0.03  
-#> -------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   race            0.326       
-#> -----------------------------
-#> 
-#> 
-#> Variable: female
-#> 
-#>                            Weight of Evidence                             
-#> -------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
-#> -------------------------------------------------------------------------
-#>   0          73          18          0.50       0.34       0.38     0.06  
-#>   1          74          35          0.50       0.66      -0.27     0.04  
-#> -------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>  female          0.1023       
-#> -----------------------------
-#> 
-#> 
-#> Variable: schtyp
-#> 
-#>                            Weight of Evidence                            
-#> ------------------------------------------------------------------------
-#> levels    count_0s    count_1s    dist_0s    dist_1s        woe     iv   
-#> ------------------------------------------------------------------------
-#>   1         123          45          0.84       0.85      -0.01    0.00  
-#>   2          24          8           0.16       0.15       0.08    0.00  
-#> ------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>  schtyp          0.0012       
-#> -----------------------------
-#> 
-#> 
-
-
    -
    - + - - - - + diff --git a/docs/reference/hsb2.html b/docs/reference/hsb2.html index be2450c7..7ef9b070 100644 --- a/docs/reference/hsb2.html +++ b/docs/reference/hsb2.html @@ -1,195 +1,118 @@ - - - - - - +High School and Beyond Data Set — hsb2 • blorr + Skip to contents -High School and Beyond Data Set — hsb2 • blorr +
    +
    +
    - - - +

    High School and Beyond Data Set

    + Source: R/blr-data-hsb.R +
    hsb2.Rd
    +
    +
    +

    A dataset containing demographic information and standardized test +scores of high school students.

    +
    +
    +

    Usage

    + 
    hsb2
    +
    +
    +

    Format

    +

    A data frame with 200 rows and 11 variables:

    id
    +

    id of the student

    - - +
    female
    +

    gender of the student

    +
    race
    +

    ethnic background of the student

    +
    ses
    +

    socio-economic status of the student

    +
    schtyp
    +

    school type

    - - - +
    prog
    +

    program type

    - +
    read
    +

    scores from test of reading

    +
    write
    +

    scores from test of writing

    +
    math
    +

    scores from test of math

    - +
    science
    +

    scores from test of science

    - -
    -
    - - - - -
    +
    honcomp
    +

    1 if write > 60, else 0

    -
    -
    -
    -

    High School and Beyond Data Set

    - Source: R/blr-data-hsb.R - -
    -
    -

    A dataset containing demographic information and standardized test -scores of high school students.

    +
    +
    +

    Source

    +

    https://www.openintro.org/data/index.php?data=hsb

    - 
    hsb2
    - - -

    Format

    - -

    A data frame with 200 rows and 11 variables:

    -
    id

    id of the student

    -
    female

    gender of the student

    -
    race

    ethnic background of the student

    -
    ses

    socio-economic status of the student

    -
    schtyp

    school type

    -
    prog

    program type

    -
    read

    scores from test of reading

    -
    write

    scores from test of writing

    -
    math

    scores from test of math

    -
    science

    scores from test of science

    -
    socst

    scores from test of social studies

    -
    honcomp

    1 if write > 60, else 0

    - -
    +
    -

    Source

    -

    http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm

    - -
    - + - - - - + diff --git a/docs/reference/index.html b/docs/reference/index.html index e42ad179..5ca4f380 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,717 +1,583 @@ - - - - - - +Package index • blorr + Skip to contents -Function reference • blorr +
    +
    +
    - - - +

    Package index

    +
    +
    +

    Shiny App

    - +
    - - - - +
    + blr_launch_app() +
    +
    Launch shiny app
    +
    +

    Bivariate Analysis

    - - -
    -
    - - - - -
    -
    -
    -
    -

    Reference

    -
    +
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    Shiny App

    -

    -
    -

    blr_launch_app()

    -

    Launch shiny app

    -

    Bivariate Analysis

    -

    -
    -

    blr_bivariate_analysis()

    -

    Bivariate analysis

    -

    blr_segment()

    -

    Event rate

    -

    blr_segment_twoway()

    -

    Two way event rate

    -

    blr_segment_dist() plot(<blr_segment_dist>)

    -

    Response distribution

    -

    blr_woe_iv() plot(<blr_woe_iv>)

    -

    WoE & IV

    -

    blr_woe_iv_stats()

    -

    Multi variable WOE & IV

    -

    Regression

    -

    -
    -

    blr_regress()

    -

    Binary logistic regression

    -

    Model Fit Statistics

    -

    -
    -

    blr_model_fit_stats()

    -

    Model fit statistics

    -

    blr_multi_model_fit_stats()

    -

    Multi model fit statistics

    -

    blr_test_lr()

    -

    Likelihood ratio test

    -

    blr_rsq_mcfadden()

    -

    McFadden's R2

    -

    blr_rsq_mcfadden_adj()

    -

    McFadden's adjusted R2

    -

    blr_rsq_cox_snell()

    -

    Cox Snell R2

    -

    blr_rsq_nagelkerke()

    -

    Cragg-Uhler (Nagelkerke) R2

    -

    blr_rsq_mckelvey_zavoina()

    -

    McKelvey Zavoina R2

    -

    blr_rsq_effron()

    -

    Effron R2

    -

    blr_rsq_count()

    -

    Count R2

    -

    blr_rsq_adj_count()

    -

    Adjusted count R2

    -

    Model Validation

    -

    -
    -

    blr_gains_table() plot(<blr_gains_table>)

    -

    Gains table & lift chart

    -

    blr_ks_chart()

    -

    KS chart

    -

    blr_test_hosmer_lemeshow()

    -

    Hosmer lemeshow test

    -

    blr_gini_index()

    -

    Gini index

    -

    blr_lorenz_curve()

    -

    Lorenz curve

    -

    blr_confusion_matrix()

    -

    Confusion matrix

    -

    blr_pairs()

    -

    Concordant & discordant pairs

    -

    blr_roc_curve()

    -

    ROC curve

    -

    blr_decile_lift_chart()

    -

    Decile lift chart

    -

    blr_decile_capture_rate()

    -

    Event rate by decile

    -

    Variable Selection

    -

    -
    -

    blr_step_aic_forward() plot(<blr_step_aic_forward>)

    -

    Stepwise AIC forward selection

    -

    blr_step_p_forward() plot(<blr_step_p_forward>)

    -

    Stepwise forward regression

    -

    blr_step_aic_backward() plot(<blr_step_aic_backward>)

    -

    Stepwise AIC backward elimination

    -

    blr_step_p_backward() plot(<blr_step_p_backward>)

    -

    Stepwise backward regression

    -

    blr_step_aic_both() plot(<blr_step_aic_both>)

    -

    Stepwise AIC selection

    -

    blr_step_p_both() plot(<blr_step_p_both>)

    -

    Stepwise regression

    -

    Plot Data

    -

    -
    -

    blr_prep_roc_data()

    -

    ROC curve data

    -

    blr_prep_lorenz_data()

    -

    Lorenz curve data

    -

    blr_prep_dcrate_data()

    -

    Decile capture rate data

    -

    blr_prep_lchart_gmean() blr_prep_lchart_data()

    -

    Lift Chart data

    -

    blr_prep_kschart_data() blr_prep_kschart_line() blr_prep_ksannotate_y() blr_prep_kschart_stat() blr_prep_ksannotate_x()

    -

    KS Chart data

    -

    Residual Diagnostics

    -

    -
    -

    blr_plot_residual_fitted()

    -

    Residual vs fitted values plot

    -

    blr_plot_pearson_residual()

    -

    Residual values plot

    -

    blr_plot_deviance_fitted()

    -

    Deviance vs fitted values plot

    -

    blr_plot_deviance_residual()

    -

    Deviance residual values

    -

    blr_plot_leverage_fitted()

    -

    Leverage vs fitted values plot

    -

    blr_plot_leverage()

    -

    Leverage plot

    -

    blr_residual_diagnostics()

    -

    Residual diagnostics

    -

    blr_plot_diag_c()

    -

    CI Displacement C plot

    -

    blr_plot_diag_cbar()

    -

    CI Displacement CBAR plot

    -

    blr_plot_diag_difchisq()

    -

    Delta chisquare plot

    -

    blr_plot_diag_difdev()

    -

    Delta deviance plot

    -

    blr_plot_dfbetas_panel()

    -

    DFBETAs panel

    -

    blr_plot_diag_influence()

    -

    Influence diagnostics plot

    -

    blr_plot_diag_fit()

    -

    Fitted values diagnostics plot

    -

    blr_plot_diag_leverage()

    -

    Leverage diagnostics plot

    -

    blr_plot_c_fitted()

    -

    CI Displacement C vs fitted values plot

    -

    blr_plot_difchisq_fitted()

    -

    Delta chi square vs fitted values plot

    -

    blr_plot_difdev_fitted()

    -

    Delta deviance vs fitted values plot

    -

    blr_plot_difchisq_leverage()

    -

    Delta chi square vs leverage plot

    -

    blr_plot_difdev_leverage()

    -

    Delta deviance vs leverage plot

    -

    blr_plot_c_leverage()

    -

    CI Displacement C vs leverage plot

    -

    blr_plot_fitted_leverage()

    -

    Fitted values vs leverage plot

    -

    Collinearity Diagnostics

    -

    -
    -

    blr_coll_diag() blr_vif_tol() blr_eigen_cindex()

    -

    Collinearity diagnostics

    -

    Model Specification Error

    -

    -
    -

    blr_linktest()

    -

    Model specification error

    -

    Data

    -

    -
    -

    hsb2

    -

    High School and Beyond Data Set

    -

    bank_marketing

    -

    Bank marketing data set

    -

    stepwise

    -

    Dummy Data Set

    -
    - -
    -
    + + + -
    + + - - - + diff --git a/docs/reference/plot.blr_gains_table.html b/docs/reference/plot.blr_gains_table.html new file mode 100644 index 00000000..0fdc4b48 --- /dev/null +++ b/docs/reference/plot.blr_gains_table.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_segment_dist.html b/docs/reference/plot.blr_segment_dist.html new file mode 100644 index 00000000..1d8eb06c --- /dev/null +++ b/docs/reference/plot.blr_segment_dist.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_aic_backward.html b/docs/reference/plot.blr_step_aic_backward.html new file mode 100644 index 00000000..05696538 --- /dev/null +++ b/docs/reference/plot.blr_step_aic_backward.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_aic_both.html b/docs/reference/plot.blr_step_aic_both.html new file mode 100644 index 00000000..6b590ebd --- /dev/null +++ b/docs/reference/plot.blr_step_aic_both.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_aic_forward.html b/docs/reference/plot.blr_step_aic_forward.html new file mode 100644 index 00000000..0827966c --- /dev/null +++ b/docs/reference/plot.blr_step_aic_forward.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_p_backward.html b/docs/reference/plot.blr_step_p_backward.html new file mode 100644 index 00000000..5c47f796 --- /dev/null +++ b/docs/reference/plot.blr_step_p_backward.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_p_both.html b/docs/reference/plot.blr_step_p_both.html new file mode 100644 index 00000000..a24cbbdb --- /dev/null +++ b/docs/reference/plot.blr_step_p_both.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_step_p_forward.html b/docs/reference/plot.blr_step_p_forward.html new file mode 100644 index 00000000..65dfb716 --- /dev/null +++ b/docs/reference/plot.blr_step_p_forward.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.blr_woe_iv.html b/docs/reference/plot.blr_woe_iv.html new file mode 100644 index 00000000..a9f6840d --- /dev/null +++ b/docs/reference/plot.blr_woe_iv.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/stepwise.html b/docs/reference/stepwise.html index a240377d..ad866d1c 100644 --- a/docs/reference/stepwise.html +++ b/docs/reference/stepwise.html @@ -1,175 +1,75 @@ - - - - - - +Dummy Data Set — stepwise • blorr + Skip to contents -Dummy Data Set — stepwise • blorr +
    -
    -
    -

    Dummy Data Set

    - Source: R/blr-data-stepwise.R - +
    + +

    Dummy Data Set

    + Source: R/blr-data-stepwise.R +
    stepwise.Rd
    -
    +

    Dummy Data Set

    - 
    stepwise
    - - -

    Format

    +
    +

    Usage

    + 
    stepwise
    +
    +
    +

    Format

    An object of class data.frame with 20000 rows and 7 columns.

    +
    -
    - -
    +
    -
    + - - - + diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 00000000..bb39a1d4 --- /dev/null +++ b/docs/search.json @@ -0,0 +1 @@ +[{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"A Short Introduction to the blorr Package","text":"blorr package offers tools building validating binary logistic regression models. suitable beginner/intermediate R users teach statistics using R. API simple functions take either data.frame/tibble model input. blorr use consistent prefix blr_ easy tab completion.","code":""},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"installation","dir":"Articles","previous_headings":"Introduction","what":"Installation","title":"A Short Introduction to the blorr Package","text":"can install blorr using: documentation package can found https://blorr.rsquaredacademy.com. vignette gives quick tour package.","code":"install.packages(\"blorr\")"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"libraries","dir":"Articles","previous_headings":"Introduction","what":"Libraries","title":"A Short Introduction to the blorr Package","text":"following libraries used examples vignette:","code":"library(blorr) library(magrittr)"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"A Short Introduction to the blorr Package","text":"demonstrate features blorr, use bank marketing data set. data related direct marketing campaigns Portuguese banking institution. marketing campaigns based phone calls. Often, one contact client required, order access product (bank term deposit) (‘yes’) (‘’) subscribed. contains random sample (~4k) original data set can found https://archive.ics.uci.edu/ml/datasets/bank+marketing.","code":""},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"bivariate-analysis","dir":"Articles","previous_headings":"","what":"Bivariate Analysis","title":"A Short Introduction to the blorr Package","text":"Let us begin careful bivariate analysis possible variable outcome variable. use information value likelihood ratio chi square test selecting initial set predictors model. bivariate analysis currently avialable categorical predictors .","code":"blr_bivariate_analysis(bank_marketing, y, job, marital, education, default, housing, loan, contact, poutcome) #> Bivariate Analysis #> ---------------------------------------------------------------------- #> Variable Information Value LR Chi Square LR DF LR p-value #> ---------------------------------------------------------------------- #> job 0.16 75.2690 11 0.0000 #> marital 0.05 21.6821 2 0.0000 #> education 0.05 25.0466 3 0.0000 #> default 0.02 6.0405 1 0.0140 #> housing 0.16 72.2813 1 0.0000 #> loan 0.06 26.6615 1 0.0000 #> contact 0.31 124.3834 2 0.0000 #> poutcome 0.53 270.6450 3 0.0000 #> ----------------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"weight-of-evidence-information-value","dir":"Articles","previous_headings":"Bivariate Analysis","what":"Weight of Evidence & Information Value","title":"A Short Introduction to the blorr Package","text":"Weight evidence (WoE) used assess relative risk di¤erent attributes characteristic means transform characteristics variables. also useful tool binning. WoE group average odds zero. negative WoE indicates proportion defaults higher attribute overall proportion indicates higher risk. information value used rank order variables terms predictive power. high information value indicates high ability discriminate. Values information value always positive may 3 assessing highly predictive characteristics. Characteristics information values less 0:10 typically viewed weak, values 0.30 sought .","code":"blr_woe_iv(bank_marketing, job, y) #> Weight of Evidence #> -------------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> -------------------------------------------------------------------------------- #> management 809 130 0.20 0.25 -0.22 0.01 #> technician 682 79 0.17 0.15 0.11 0.00 #> entrepreneur 139 12 0.03 0.02 0.40 0.00 #> blue-collar 937 73 0.23 0.14 0.51 0.05 #> unknown 29 2 0.01 0.00 0.61 0.00 #> retired 152 47 0.04 0.09 -0.87 0.05 #> admin. 433 61 0.11 0.12 -0.09 0.00 #> services 392 39 0.10 0.08 0.26 0.01 #> self-employed 132 22 0.03 0.04 -0.26 0.00 #> unemployed 126 15 0.03 0.03 0.08 0.00 #> housemaid 110 12 0.03 0.02 0.17 0.00 #> student 63 25 0.02 0.05 -1.13 0.04 #> -------------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> job 0.1594 #> -----------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"plot","dir":"Articles","previous_headings":"Bivariate Analysis > Weight of Evidence & Information Value","what":"Plot","title":"A Short Introduction to the blorr Package","text":"","code":"k <- blr_woe_iv(bank_marketing, job, y) plot(k)"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"multiple-variables","dir":"Articles","previous_headings":"Bivariate Analysis > Weight of Evidence & Information Value","what":"Multiple Variables","title":"A Short Introduction to the blorr Package","text":"can generate weight evidence information value multiple variables using blr_woe_iv_stats(). blr_woe_iv() blr_woe_iv_stats() currently avialable categorical predictors .","code":"blr_woe_iv_stats(bank_marketing, y, job, marital, education) #> Variable: job #> #> Weight of Evidence #> -------------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> -------------------------------------------------------------------------------- #> management 809 130 0.20 0.25 -0.22 0.01 #> technician 682 79 0.17 0.15 0.11 0.00 #> entrepreneur 139 12 0.03 0.02 0.40 0.00 #> blue-collar 937 73 0.23 0.14 0.51 0.05 #> unknown 29 2 0.01 0.00 0.61 0.00 #> retired 152 47 0.04 0.09 -0.87 0.05 #> admin. 433 61 0.11 0.12 -0.09 0.00 #> services 392 39 0.10 0.08 0.26 0.01 #> self-employed 132 22 0.03 0.04 -0.26 0.00 #> unemployed 126 15 0.03 0.03 0.08 0.00 #> housemaid 110 12 0.03 0.02 0.17 0.00 #> student 63 25 0.02 0.05 -1.13 0.04 #> -------------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> job 0.1594 #> ----------------------------- #> #> #> Variable: marital #> #> Weight of Evidence #> --------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> --------------------------------------------------------------------------- #> married 2467 273 0.62 0.53 0.15 0.01 #> single 1079 191 0.27 0.37 -0.32 0.03 #> divorced 458 53 0.11 0.10 0.11 0.00 #> --------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> marital 0.0464 #> ----------------------------- #> #> #> Variable: education #> #> Weight of Evidence #> ---------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ---------------------------------------------------------------------------- #> tertiary 1104 195 0.28 0.38 -0.31 0.03 #> secondary 2121 231 0.53 0.45 0.17 0.01 #> unknown 154 25 0.04 0.05 -0.23 0.00 #> primary 625 66 0.16 0.13 0.20 0.01 #> ---------------------------------------------------------------------------- #> #> Information Value #> ------------------------------ #> Variable Information Value #> ------------------------------ #> education 0.0539 #> ------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"stepwise-selection","dir":"Articles","previous_headings":"","what":"Stepwise Selection","title":"A Short Introduction to the blorr Package","text":"initial/ first cut model, independent variables put model. goal include limited number independent variables (5-15) significant, without sacrificing much model performance. rationale behind -including many variables model fitted become unstable tested validation sample. variable reduction done using forward backward stepwise variable selection procedures. use blr_step_aic_both() shortlist predictors model.","code":""},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"model","dir":"Articles","previous_headings":"Stepwise Selection","what":"Model","title":"A Short Introduction to the blorr Package","text":"","code":"model <- glm(y ~ ., data = bank_marketing, family = binomial(link = 'logit'))"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"selection-summary","dir":"Articles","previous_headings":"Stepwise Selection > Model","what":"Selection Summary","title":"A Short Introduction to the blorr Package","text":"","code":"blr_step_aic_both(model) #> Stepwise Selection Method #> ------------------------- #> #> Candidate Terms: #> #> 1 . age #> 2 . job #> 3 . marital #> 4 . education #> 5 . default #> 6 . balance #> 7 . housing #> 8 . loan #> 9 . contact #> 10 . day #> 11 . month #> 12 . duration #> 13 . campaign #> 14 . pdays #> 15 . previous #> 16 . poutcome #> #> #> Variables Entered/Removed: #> #> - duration added #> - poutcome added #> - month added #> - contact added #> - housing added #> - loan added #> - campaign added #> - marital added #> - education added #> - age added #> #> No more variables to be added or removed. #> #> #> Stepwise Summary #> --------------------------------------------------------- #> Variable Method AIC BIC Deviance #> --------------------------------------------------------- #> duration addition 2674.384 2687.217 2670.384 #> poutcome addition 2396.014 2428.097 2386.014 #> month addition 2274.109 2376.773 2242.109 #> contact addition 2207.884 2323.381 2171.884 #> housing addition 2184.550 2306.463 2146.550 #> loan addition 2171.972 2300.302 2131.972 #> campaign addition 2164.164 2298.910 2122.164 #> marital addition 2158.524 2306.103 2112.524 #> education addition 2155.837 2322.666 2103.837 #> age addition 2154.272 2327.517 2100.272 #> ---------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"plot-1","dir":"Articles","previous_headings":"Stepwise Selection > Model","what":"Plot","title":"A Short Introduction to the blorr Package","text":"","code":"model %>% blr_step_aic_both() %>% plot() #> Stepwise Selection Method #> ------------------------- #> #> Candidate Terms: #> #> 1 . age #> 2 . job #> 3 . marital #> 4 . education #> 5 . default #> 6 . balance #> 7 . housing #> 8 . loan #> 9 . contact #> 10 . day #> 11 . month #> 12 . duration #> 13 . campaign #> 14 . pdays #> 15 . previous #> 16 . poutcome #> #> #> Variables Entered/Removed: #> #> - duration added #> - poutcome added #> - month added #> - contact added #> - housing added #> - loan added #> - campaign added #> - marital added #> - education added #> - age added #> #> No more variables to be added or removed."},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"model-1","dir":"Articles","previous_headings":"Regression Output","what":"Model","title":"A Short Introduction to the blorr Package","text":"can use bivariate analysis stepwise selection procedure shortlist predictors build model using glm(). predictors used model illustration purposes necessarily shortlisted bivariate analysis variable selection procedures. Use blr_regress() generate comprehensive regression output. accepts either following model built using glm() model formula data","code":"model <- glm(y ~ age + duration + previous + housing + default + loan + poutcome + job + marital, data = bank_marketing, family = binomial(link = 'logit'))"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"using-model","dir":"Articles","previous_headings":"Regression Output > Model","what":"Using Model","title":"A Short Introduction to the blorr Package","text":"Let us look output generated blr_regress(): want examine odds ratio estimates, set odd_conf_limit TRUE. odds ratio estimates explicitly computed observed considerable increase computation time dealing large data sets.","code":"blr_regress(model) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data y 4521 4520 4498 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 4004 1 517 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------------- #> (Intercept) 1 -5.1347 0.3728 -13.7729 0.0000 #> age 1 0.0096 0.0067 1.4299 0.1528 #> duration 1 0.0042 2e-04 20.7853 0.0000 #> previous 1 -0.0357 0.0392 -0.9089 0.3634 #> housingno 1 0.7894 0.1232 6.4098 0.0000 #> defaultyes 1 -0.8691 0.6919 -1.2562 0.2091 #> loanno 1 0.6598 0.1945 3.3925 7e-04 #> poutcomefailure 1 0.6085 0.2012 3.0248 0.0025 #> poutcomeother 1 1.1354 0.2700 4.2057 0.0000 #> poutcomesuccess 1 3.2481 0.2462 13.1913 0.0000 #> jobtechnician 1 -0.2713 0.1806 -1.5019 0.1331 #> jobentrepreneur 1 -0.7041 0.3809 -1.8486 0.0645 #> jobblue-collar 1 -0.6132 0.1867 -3.2851 0.0010 #> jobunknown 1 -0.9932 0.8226 -1.2073 0.2273 #> jobretired 1 0.3197 0.2729 1.1713 0.2415 #> jobadmin. 1 0.1120 0.2001 0.5599 0.5755 #> jobservices 1 -0.1750 0.2265 -0.7728 0.4397 #> jobself-employed 1 -0.1408 0.3009 -0.4680 0.6398 #> jobunemployed 1 -0.6581 0.3432 -1.9174 0.0552 #> jobhousemaid 1 -0.7456 0.3932 -1.8963 0.0579 #> jobstudent 1 0.1927 0.3433 0.5613 0.5746 #> maritalsingle 1 0.5451 0.1387 3.9299 1e-04 #> maritaldivorced 1 -0.1989 0.1986 -1.0012 0.3167 #> ----------------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8886 Somers' D 0.7773 #> % Discordant 0.1114 Gamma 0.7773 #> % Tied 0.0000 Tau-a 0.1575 #> Pairs 2070068 c 0.8886 #> ---------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"using-formula","dir":"Articles","previous_headings":"Regression Output > Model","what":"Using Formula","title":"A Short Introduction to the blorr Package","text":"Let us use model formula data set generate results.","code":"blr_regress(y ~ age + duration + previous + housing + default + loan + poutcome + job + marital, data = bank_marketing) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data y 4521 4520 4498 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 4004 1 517 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------------- #> (Intercept) 1 -5.1347 0.3728 -13.7729 0.0000 #> age 1 0.0096 0.0067 1.4299 0.1528 #> duration 1 0.0042 2e-04 20.7853 0.0000 #> previous 1 -0.0357 0.0392 -0.9089 0.3634 #> housingno 1 0.7894 0.1232 6.4098 0.0000 #> defaultyes 1 -0.8691 0.6919 -1.2562 0.2091 #> loanno 1 0.6598 0.1945 3.3925 7e-04 #> poutcomefailure 1 0.6085 0.2012 3.0248 0.0025 #> poutcomeother 1 1.1354 0.2700 4.2057 0.0000 #> poutcomesuccess 1 3.2481 0.2462 13.1913 0.0000 #> jobtechnician 1 -0.2713 0.1806 -1.5019 0.1331 #> jobentrepreneur 1 -0.7041 0.3809 -1.8486 0.0645 #> jobblue-collar 1 -0.6132 0.1867 -3.2851 0.0010 #> jobunknown 1 -0.9932 0.8226 -1.2073 0.2273 #> jobretired 1 0.3197 0.2729 1.1713 0.2415 #> jobadmin. 1 0.1120 0.2001 0.5599 0.5755 #> jobservices 1 -0.1750 0.2265 -0.7728 0.4397 #> jobself-employed 1 -0.1408 0.3009 -0.4680 0.6398 #> jobunemployed 1 -0.6581 0.3432 -1.9174 0.0552 #> jobhousemaid 1 -0.7456 0.3932 -1.8963 0.0579 #> jobstudent 1 0.1927 0.3433 0.5613 0.5746 #> maritalsingle 1 0.5451 0.1387 3.9299 1e-04 #> maritaldivorced 1 -0.1989 0.1986 -1.0012 0.3167 #> ----------------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8886 Somers' D 0.7773 #> % Discordant 0.1114 Gamma 0.7773 #> % Tied 0.0000 Tau-a 0.1575 #> Pairs 2070068 c 0.8886 #> ---------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"model-fit-statistics","dir":"Articles","previous_headings":"","what":"Model Fit Statistics","title":"A Short Introduction to the blorr Package","text":"Model fit statistics available assess well model fits data compare two different models.output includes likelihood ratio test, AIC, BIC host pseudo r-squared measures. can read pseudo r-squared .","code":""},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"single-model","dir":"Articles","previous_headings":"Model Fit Statistics","what":"Single Model","title":"A Short Introduction to the blorr Package","text":"","code":"blr_model_fit_stats(model) #> Model Fit Statistics #> ---------------------------------------------------------------------------------- #> Log-Lik Intercept Only: -1607.330 Log-Lik Full Model: -1123.340 #> Deviance(4498): 2246.679 LR(22): 967.980 #> Prob > LR: 0.000 #> MCFadden's R2 0.301 McFadden's Adj R2: 0.287 #> ML (Cox-Snell) R2: 0.193 Cragg-Uhler(Nagelkerke) R2: 0.379 #> McKelvey & Zavoina's R2: 0.388 Efron's R2: 0.278 #> Count R2: 0.904 Adj Count R2: 0.157 #> BIC: 2440.259 AIC: 2292.679 #> ----------------------------------------------------------------------------------"},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"confusion-matrix","dir":"Articles","previous_headings":"Model Validation","what":"Confusion Matrix","title":"A Short Introduction to the blorr Package","text":"event deciding cut-point probability scores logistic regression model, confusion matrix created corresponding particular cut-. observations probability scores cut-score predicted events cut-score, non-events. confusion matrix, 2X2 table, calculates number correctly classified miss-classified observations. validity cut-measured using sensitivity, specificity accuracy. Sensitivity: % correctly classified events events = TP / (TP + FN) Specificity: % correctly classified non-events non-events = TN / (TN + FP) Accuracy: % correctly classified observation observations = (TP + TN) / (TP + FP + TN + FN) True Positive (TP) : Events correctly classified events. True Negative (TN) : Non-Events correctly classified non-events. False Positive (FP): Non-events miss-classified events. False Negative (FN): Events miss-classified non-events. standard logistic model, higher cut-, lower sensitivity higher specificity. cut-decreased, sensitivity go , events captured. Also, specificity go , non-events miss-classified events. Hence trade-done based requirements. example, looking capture many events possible, can afford miss-classified non-events, low cut-taken.","code":"blr_confusion_matrix(model, cutoff = 0.5) #> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 3920 352 #> 1 84 165 #> #> #> Accuracy : 0.9036 #> No Information Rate : 0.8856 #> #> Kappa : 0.3851 #> #> McNemars's Test P-Value : 0.0000 #> #> Sensitivity : 0.3191 #> Specificity : 0.9790 #> Pos Pred Value : 0.6627 #> Neg Pred Value : 0.9176 #> Prevalence : 0.1144 #> Detection Rate : 0.0365 #> Detection Prevalence : 0.0551 #> Balanced Accuracy : 0.6491 #> Precision : 0.6627 #> Recall : 0.3191 #> #> 'Positive' Class : 1"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"hosmer-lemeshow-test","dir":"Articles","previous_headings":"Model Validation","what":"Hosmer Lemeshow Test","title":"A Short Introduction to the blorr Package","text":"Hosmer Lemeshow developed goodness--fit test logistic regression models binary responses. test involves dividing data approximately ten groups roughly equal size based percentiles estimated probabilities. observations sorted increasing order estimated probability even outcome. discrepancies observed expected number observations groups summarized Pearson chi-square statistic, compared chi-square distribution t degrees freedom, t number groups minus 2. Lower values Goodness--fit preferred.","code":"blr_test_hosmer_lemeshow(model) #> Partition for the Hosmer & Lemeshow Test #> -------------------------------------------------------------- #> def = 1 def = 0 #> Group Total Observed Expected Observed Expected #> -------------------------------------------------------------- #> 1 453 2 5.14 451 447.86 #> 2 452 3 8.63 449 443.37 #> 3 452 4 11.88 448 440.12 #> 4 452 7 15.29 445 436.71 #> 5 452 14 19.39 438 432.61 #> 6 452 10 24.97 442 427.03 #> 7 452 31 33.65 421 418.35 #> 8 452 62 49.74 390 402.26 #> 9 452 128 88.10 324 363.90 #> 10 452 256 260.21 196 191.79 #> -------------------------------------------------------------- #> #> Goodness of Fit Test #> ------------------------------ #> Chi-Square DF Pr > ChiSq #> ------------------------------ #> 52.9942 8 0.0000 #> ------------------------------"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"gains-table-lift-chart","dir":"Articles","previous_headings":"Model Validation","what":"Gains Table & Lift Chart","title":"A Short Introduction to the blorr Package","text":"lift curve graphical representation % cumulative events captured specific cut-. cut-can particular decile percentile. Similar, rank ordering procedure, data descending order scores grouped deciles/percentiles. cumulative number observations events computed decile/percentile. lift curve created using cumulative % population x-axis cumulative percentage events y-axis.","code":"blr_gains_table(model) #> decile total 1 0 ks tp tn fp fn sensitivity specificity #> 1 1 452 256 196 44.62134 256 3808 196 261 49.51644 95.10490 #> 2 2 452 128 324 61.28765 384 3484 520 133 74.27466 87.01299 #> 3 3 452 62 390 63.53965 446 3094 910 71 86.26692 77.27273 #> 4 4 452 31 421 59.02130 477 2673 1331 40 92.26306 66.75824 #> 5 5 452 10 442 49.91657 487 2231 1773 30 94.19729 55.71928 #> 6 6 452 14 438 41.68544 501 1793 2211 16 96.90522 44.78022 #> 7 7 452 7 445 31.92552 508 1348 2656 9 98.25919 33.66633 #> 8 8 452 4 448 21.51040 512 900 3104 5 99.03288 22.47752 #> 9 9 452 3 449 10.87689 515 451 3553 2 99.61315 11.26374 #> 10 10 453 2 451 0.00000 517 0 4004 0 100.00000 0.00000 #> accuracy #> 1 89.89162 #> 2 85.55629 #> 3 78.30126 #> 4 69.67485 #> 5 60.11944 #> 6 50.74099 #> 7 41.05286 #> 8 31.23203 #> 9 21.36695 #> 10 11.43552"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"lift-chart","dir":"Articles","previous_headings":"Model Validation > Gains Table & Lift Chart","what":"Lift Chart","title":"A Short Introduction to the blorr Package","text":"","code":"model %>% blr_gains_table() %>% plot()"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"roc-curve","dir":"Articles","previous_headings":"Model Validation","what":"ROC Curve","title":"A Short Introduction to the blorr Package","text":"ROC curve graphical representation validity cut-offs logistic regression model. ROC curve plotted using sensitivity specificity possible cut-offs, .e., probability scores. graph plotted using sensitivity y-axis 1-specificity x-axis. point ROC curve represents sensitivity X (1-specificity) measure corresponding cut-. area ROC curve used validation measure model – bigger area better model.","code":"model %>% blr_gains_table() %>% blr_roc_curve()"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"ks-chart","dir":"Articles","previous_headings":"Model Validation","what":"KS Chart","title":"A Short Introduction to the blorr Package","text":"KS Statistic measure model efficiency, created using lift curve. lift curve created plot % events. also plot % non-events scale, % population x-axis, get another curve. maximum distance lift curve events non-events termed KS. good model, KS big (>=0.3) occur close event rate possible.","code":"model %>% blr_gains_table() %>% blr_ks_chart()"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"decile-lift-chart","dir":"Articles","previous_headings":"Model Validation","what":"Decile Lift Chart","title":"A Short Introduction to the blorr Package","text":"decile lift chart displays lift global mean event rate decile. model good discriminatory power, top deciles event/conversion rate greater global mean.","code":"model %>% blr_gains_table() %>% blr_decile_lift_chart()"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"capture-rate-by-decile","dir":"Articles","previous_headings":"Model Validation","what":"Capture Rate by Decile","title":"A Short Introduction to the blorr Package","text":"model good discriminatory power, top deciles higher event/conversion rate compared bottom deciles.","code":"model %>% blr_gains_table() %>% blr_decile_capture_rate()"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"lorenz-curve","dir":"Articles","previous_headings":"Model Validation","what":"Lorenz Curve","title":"A Short Introduction to the blorr Package","text":"Lorenz curve simple graphic device illustrates degree inequality distribution thevariable concerned. visual representation inequality used measure discriminatory power predictive model.","code":"blr_lorenz_curve(model)"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"residual-influence-diagnostics","dir":"Articles","previous_headings":"","what":"Residual & Influence Diagnostics","title":"A Short Introduction to the blorr Package","text":"blorr can generate 22 plots residual, influence leverage diagnostics.","code":""},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"influence-diagnostics","dir":"Articles","previous_headings":"Residual & Influence Diagnostics","what":"Influence Diagnostics","title":"A Short Introduction to the blorr Package","text":"","code":"blr_plot_diag_influence(model)"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"leverage-diagnostics","dir":"Articles","previous_headings":"Residual & Influence Diagnostics","what":"Leverage Diagnostics","title":"A Short Introduction to the blorr Package","text":"","code":"blr_plot_diag_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/articles/introduction.html","id":"fitted-values-diagnostics","dir":"Articles","previous_headings":"Residual & Influence Diagnostics","what":"Fitted Values Diagnostics","title":"A Short Introduction to the blorr Package","text":"","code":"blr_plot_diag_fit(model)"},{"path":"https://blorr.rsquaredacademy.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Aravind Hebbali. Author, maintainer.","code":""},{"path":"https://blorr.rsquaredacademy.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hebbali (2024). blorr: Tools Developing Binary Logistic Regression Models. R package version 0.3.1.9000, https://github.com/rsquaredacademy/blorr, https://blorr.rsquaredacademy.com/.","code":"@Manual{, title = {blorr: Tools for Developing Binary Logistic Regression Models}, author = {Aravind Hebbali}, year = {2024}, note = {R package version 0.3.1.9000, https://github.com/rsquaredacademy/blorr}, url = {https://blorr.rsquaredacademy.com/}, }"},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement hebbali.aravind@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://blorr.rsquaredacademy.com/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://blorr.rsquaredacademy.com/CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (http:contributor-covenant.org), version 1.0.0, available http://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"blorr","dir":"","previous_headings":"","what":"Tools for Developing Binary Logistic Regression Models","title":"Tools for Developing Binary Logistic Regression Models","text":"Tools building binary logistic regression models","code":""},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Tools for Developing Binary Logistic Regression Models","text":"Tools designed make easier users, particularly beginner/intermediate R users build logistic regression models. Includes comprehensive regression output, variable selection procedures, model validation techniques ‘shiny’ app interactive model building.","code":""},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"# Install blorr from CRAN install.packages(\"blorr\") # Install development version from GitHub # install.packages(\"devtools\") devtools::install_github(\"rsquaredacademy/blorr\") # Install the development version from `rsquaredacademy` universe install.packages(\"blorr\", repos = \"https://rsquaredacademy.r-universe.dev\")"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"articles","dir":"","previous_headings":"","what":"Articles","title":"Tools for Developing Binary Logistic Regression Models","text":"Short Introduction blorr Package","code":""},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Tools for Developing Binary Logistic Regression Models","text":"blorr uses consistent prefix blr_* easy tab completion.","code":"library(blorr) library(magrittr)"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"bivariate-analysis","dir":"","previous_headings":"Usage","what":"Bivariate Analysis","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp) #> Bivariate Analysis #> --------------------------------------------------------------------- #> Variable Information Value LR Chi Square LR DF LR p-value #> --------------------------------------------------------------------- #> female 0.10 3.9350 1 0.0473 #> prog 0.43 16.1450 2 3e-04 #> race 0.33 11.3694 3 0.0099 #> schtyp 0.00 0.0445 1 0.8330 #> ---------------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"weight-of-evidence--information-value","dir":"","previous_headings":"Usage","what":"Weight of Evidence & Information Value","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_woe_iv(hsb2, prog, honcomp) #> Weight of Evidence #> ------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 38 7 0.26 0.13 0.67 0.08 #> 2 65 40 0.44 0.75 -0.53 0.17 #> 3 44 6 0.30 0.11 0.97 0.18 #> ------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> prog 0.4329 #> -----------------------------"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"model","dir":"","previous_headings":"Usage","what":"Model","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"# create model using glm model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit'))"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"regression-output","dir":"","previous_headings":"Usage","what":"Regression Output","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_regress(model) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> ---------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"model-fit-statistics","dir":"","previous_headings":"Usage","what":"Model Fit Statistics","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_model_fit_stats(model) #> Model Fit Statistics #> --------------------------------------------------------------------------------- #> Log-Lik Intercept Only: -115.644 Log-Lik Full Model: -80.118 #> Deviance(196): 160.236 LR(3): 71.052 #> Prob > LR: 0.000 #> MCFadden's R2 0.307 McFadden's Adj R2: 0.273 #> ML (Cox-Snell) R2: 0.299 Cragg-Uhler(Nagelkerke) R2: 0.436 #> McKelvey & Zavoina's R2: 0.518 Efron's R2: 0.330 #> Count R2: 0.810 Adj Count R2: 0.283 #> BIC: 181.430 AIC: 168.236 #> ---------------------------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"confusion-matrix","dir":"","previous_headings":"Usage","what":"Confusion Matrix","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_confusion_matrix(model) #> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 135 26 #> 1 12 27 #> #> #> Accuracy : 0.8100 #> No Information Rate : 0.7350 #> #> Kappa : 0.4673 #> #> McNemars's Test P-Value : 0.0350 #> #> Sensitivity : 0.5094 #> Specificity : 0.9184 #> Pos Pred Value : 0.6923 #> Neg Pred Value : 0.8385 #> Prevalence : 0.2650 #> Detection Rate : 0.1350 #> Detection Prevalence : 0.1950 #> Balanced Accuracy : 0.7139 #> Precision : 0.6923 #> Recall : 0.5094 #> #> 'Positive' Class : 1"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"hosmer-lemeshow-test","dir":"","previous_headings":"Usage","what":"Hosmer Lemeshow Test","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_test_hosmer_lemeshow(model) #> Partition for the Hosmer & Lemeshow Test #> -------------------------------------------------------------- #> def = 1 def = 0 #> Group Total Observed Expected Observed Expected #> -------------------------------------------------------------- #> 1 20 0 0.16 20 19.84 #> 2 20 0 0.53 20 19.47 #> 3 20 2 0.99 18 19.01 #> 4 20 1 1.64 19 18.36 #> 5 21 3 2.72 18 18.28 #> 6 19 3 4.05 16 14.95 #> 7 20 7 6.50 13 13.50 #> 8 20 10 8.90 10 11.10 #> 9 20 13 11.49 7 8.51 #> 10 20 14 16.02 6 3.98 #> -------------------------------------------------------------- #> #> Goodness of Fit Test #> ------------------------------ #> Chi-Square DF Pr > ChiSq #> ------------------------------ #> 4.4998 8 0.8095 #> ------------------------------"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"gains-table","dir":"","previous_headings":"Usage","what":"Gains Table","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_gains_table(model) #> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy #> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 #> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 #> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 #> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 #> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 #> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 #> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 #> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 #> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 #> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"lift-chart","dir":"","previous_headings":"Usage","what":"Lift Chart","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"model %>% blr_gains_table() %>% plot()"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"roc-curve","dir":"","previous_headings":"Usage","what":"ROC Curve","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"model %>% blr_gains_table() %>% blr_roc_curve()"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"ks-chart","dir":"","previous_headings":"Usage","what":"KS Chart","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"model %>% blr_gains_table() %>% blr_ks_chart()"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"lorenz-curve","dir":"","previous_headings":"Usage","what":"Lorenz Curve","title":"Tools for Developing Binary Logistic Regression Models","text":"","code":"blr_lorenz_curve(model)"},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Tools for Developing Binary Logistic Regression Models","text":"encounter bug, please file minimal reproducible example using reprex github. questions clarifications, use StackOverflow.","code":""},{"path":"https://blorr.rsquaredacademy.com/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Tools for Developing Binary Logistic Regression Models","text":"Please note blorr project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://blorr.rsquaredacademy.com/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2017 Aravind Hebbali Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/bank_marketing.html","id":null,"dir":"Reference","previous_headings":"","what":"Bank marketing data set — bank_marketing","title":"Bank marketing data set — bank_marketing","text":"data related direct marketing campaigns Portuguese banking institution. marketing campaigns based phone calls. Often, one contact client required, order access product (bank term deposit) ('yes') ('') subscribed.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/bank_marketing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bank marketing data set — bank_marketing","text":"","code":"bank_marketing"},{"path":"https://blorr.rsquaredacademy.com/reference/bank_marketing.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Bank marketing data set — bank_marketing","text":"tibble 4521 rows 17 variables: age age client job type job marital marital status education education level client default credit default? housing housing loan? loan personal loan? contact contact communication type month last contact month year day_of_week last contact day week duration last contact duration, seconds campaign number contacts performed campaign client pdays number days passed client last contacted previous campaign previous number contacts performed campaign clien poutcome outcome previous marketing campaign y client subscribed term deposit?","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/bank_marketing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bank marketing data set — bank_marketing","text":"[Moro et al., 2014] S. Moro, P. Cortez P. Rita. Data-Driven Approach Predict Success Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blorr.html","id":null,"dir":"Reference","previous_headings":"","what":"blorr package — blorr","title":"blorr package — blorr","text":"Tools developing binary logistic regression models","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blorr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"blorr package — blorr","text":"See README GitHub","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blorr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"blorr package — blorr","text":"Maintainer: Aravind Hebbali hebbali.aravind@gmail.com (ORCID)","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_bivariate_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Bivariate analysis — blr_bivariate_analysis","title":"Bivariate analysis — blr_bivariate_analysis","text":"Information value likelihood ratio chi square test initial variable/predictor selection. Currently avialable categorical predictors .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_bivariate_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bivariate analysis — blr_bivariate_analysis","text":"","code":"blr_bivariate_analysis(data, response, ...) # Default S3 method blr_bivariate_analysis(data, response, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_bivariate_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bivariate analysis — blr_bivariate_analysis","text":"data tibble data.frame. response Response variable; column data. ... Predictor variables; columns data.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_bivariate_analysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bivariate analysis — blr_bivariate_analysis","text":"tibble following columns: Variable Variable name Information Value Information value LR Chi Square Likelihood ratio statisitc LR DF Likelihood ratio degrees freedom LR p-value Likelihood ratio p value","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_bivariate_analysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bivariate analysis — blr_bivariate_analysis","text":"","code":"blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp) #> Bivariate Analysis #> --------------------------------------------------------------------- #> Variable Information Value LR Chi Square LR DF LR p-value #> --------------------------------------------------------------------- #> female 0.10 3.9350 1 0.0473 #> prog 0.43 16.1450 2 3e-04 #> race 0.33 11.3694 3 0.0099 #> schtyp 0.00 0.0445 1 0.8330 #> ---------------------------------------------------------------------"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Collinearity diagnostics — blr_coll_diag","title":"Collinearity diagnostics — blr_coll_diag","text":"Variance inflation factor, tolerance, eigenvalues condition indices.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collinearity diagnostics — blr_coll_diag","text":"","code":"blr_coll_diag(model) blr_vif_tol(model) blr_eigen_cindex(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collinearity diagnostics — blr_coll_diag","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collinearity diagnostics — blr_coll_diag","text":"blr_coll_diag returns object class \"blr_coll_diag\". object class \"blr_coll_diag\" list containing following components: vif_t tolerance variance inflation factors eig_cindex eigen values condition index","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Collinearity diagnostics — blr_coll_diag","text":"Collinearity implies two variables near perfect linear combinations one another. Multicollinearity involves two variables. presence multicollinearity, regression estimates unstable high standard errors. Tolerance Percent variance predictor accounted predictors. Variance Inflation Factor Variance inflation factors measure inflation variances parameter estimates due collinearities exist among predictors. measure much variance estimated regression coefficient \\(\\beta_k\\) inflated existence correlation among predictor variables model. VIF 1 means correlation among kth predictor remaining predictor variables, hence variance \\(\\beta_k\\) inflated . general rule thumb VIFs exceeding 4 warrant investigation, VIFs exceeding 10 signs serious multicollinearity requiring correction. Condition Index multivariate statistical approaches involve decomposing correlation matrix linear combinations variables. linear combinations chosen first combination largest possible variance (subject restrictions), second combination next largest variance, subject uncorrelated first, third largest possible variance, subject uncorrelated first second, forth. variance linear combinations called eigenvalue. Collinearity spotted finding 2 variables large proportions variance (.50 ) correspond large condition indices. rule thumb label large condition indices range 30 larger.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Collinearity diagnostics — blr_coll_diag","text":"Belsley, D. ., Kuh, E., Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data Sources Collinearity. New York: John Wiley & Sons.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_coll_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collinearity diagnostics — blr_coll_diag","text":"","code":"# model model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) # vif and tolerance blr_vif_tol(model) #> Variable Tolerance VIF #> 1 female1 0.9824347 1.017879 #> 2 read 0.6021370 1.660752 #> 3 science 0.5939847 1.683545 # eigenvalues and condition indices blr_eigen_cindex(model) #> Eigenvalue Condition Index intercept female1 read #> 1 3.57391760 1.000000 3.500782e-06 2.232611e-05 0.0006577134 #> 2 0.39409893 3.011408 5.155657e-06 8.559696e-04 0.0015266309 #> 3 0.01888407 13.757025 1.632498e-03 4.397949e-05 0.1143698337 #> 4 0.01309940 16.517583 5.619292e-05 1.312420e-05 0.2758987745 #> science #> 1 0.0009448623 #> 2 0.0025603607 #> 3 0.0545537252 #> 4 0.5468553525 # collinearity diagnostics blr_coll_diag(model) #> Tolerance and Variance Inflation Factor #> --------------------------------------- #> Variable Tolerance VIF #> 1 female1 0.9824347 1.017879 #> 2 read 0.6021370 1.660752 #> 3 science 0.5939847 1.683545 #> #> #> Eigenvalue and Condition Index #> ------------------------------ #> Eigenvalue Condition Index intercept female1 read #> 1 3.57391760 1.000000 3.500782e-06 2.232611e-05 0.0006577134 #> 2 0.39409893 3.011408 5.155657e-06 8.559696e-04 0.0015266309 #> 3 0.01888407 13.757025 1.632498e-03 4.397949e-05 0.1143698337 #> 4 0.01309940 16.517583 5.619292e-05 1.312420e-05 0.2758987745 #> science #> 1 0.0009448623 #> 2 0.0025603607 #> 3 0.0545537252 #> 4 0.5468553525"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_confusion_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Confusion matrix — blr_confusion_matrix","title":"Confusion matrix — blr_confusion_matrix","text":"Confusion matrix statistics.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_confusion_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confusion matrix — blr_confusion_matrix","text":"","code":"blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...) # Default S3 method blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_confusion_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confusion matrix — blr_confusion_matrix","text":"model object class glm. cutoff Cutoff classification. data tibble data.frame. ... arguments.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_confusion_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confusion matrix — blr_confusion_matrix","text":"Confusion matix.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_confusion_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confusion matrix — blr_confusion_matrix","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_confusion_matrix(model, cutoff = 0.4) #> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 125 16 #> 1 22 37 #> #> #> Accuracy : 0.8100 #> No Information Rate : 0.7350 #> #> Kappa : 0.5293 #> #> McNemars's Test P-Value : 0.4173 #> #> Sensitivity : 0.6981 #> Specificity : 0.8503 #> Pos Pred Value : 0.6271 #> Neg Pred Value : 0.8865 #> Prevalence : 0.2650 #> Detection Rate : 0.1850 #> Detection Prevalence : 0.2950 #> Balanced Accuracy : 0.7742 #> Precision : 0.6271 #> Recall : 0.6981 #> #> 'Positive' Class : 1"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_capture_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Event rate by decile — blr_decile_capture_rate","title":"Event rate by decile — blr_decile_capture_rate","text":"Visualize decile wise event rate.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_capture_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event rate by decile — blr_decile_capture_rate","text":"","code":"blr_decile_capture_rate( gains_table, xaxis_title = \"Decile\", yaxis_title = \"Capture Rate\", title = \"Capture Rate by Decile\", bar_color = \"blue\", text_size = 3.5, text_vjust = -0.3, print_plot = TRUE )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_capture_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event rate by decile — blr_decile_capture_rate","text":"gains_table object class blr_gains_table. xaxis_title X axis title. yaxis_title Y axis title. title Plot title. bar_color Bar color. text_size Size bar labels. text_vjust Vertical justification bar labels. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_capture_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event rate by decile — blr_decile_capture_rate","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_decile_capture_rate(gt)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_lift_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Decile lift chart — blr_decile_lift_chart","title":"Decile lift chart — blr_decile_lift_chart","text":"Decile wise lift chart.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_lift_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decile lift chart — blr_decile_lift_chart","text":"","code":"blr_decile_lift_chart( gains_table, xaxis_title = \"Decile\", yaxis_title = \"Decile Mean / Global Mean\", title = \"Decile Lift Chart\", bar_color = \"blue\", text_size = 3.5, text_vjust = -0.3, print_plot = TRUE )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_lift_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decile lift chart — blr_decile_lift_chart","text":"gains_table object class blr_gains_table. xaxis_title X axis title. yaxis_title Y axis title. title Plot title. bar_color Color bars. text_size Size bar labels. text_vjust Vertical justification bar labels. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_decile_lift_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decile lift chart — blr_decile_lift_chart","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_decile_lift_chart(gt)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Gains table & lift chart — blr_gains_table","title":"Gains table & lift chart — blr_gains_table","text":"Compute sensitivity, specificity, accuracy KS statistics generate lift chart KS chart.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gains table & lift chart — blr_gains_table","text":"","code":"blr_gains_table(model, data = NULL) # S3 method for class 'blr_gains_table' plot( x, title = \"Lift Chart\", xaxis_title = \"% Population\", yaxis_title = \"% Cumulative 1s\", diag_line_col = \"red\", lift_curve_col = \"blue\", plot_title_justify = 0.5, print_plot = TRUE, ... )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gains table & lift chart — blr_gains_table","text":"model object class glm. data tibble data.frame. x object class blr_gains_table. title Plot title. xaxis_title X axis title. yaxis_title Y axis title. diag_line_col Diagonal line color. lift_curve_col Color lift curve. plot_title_justify Horizontal justification plot title. print_plot logical; TRUE, prints plot else returns plot object. ... inputs.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gains table & lift chart — blr_gains_table","text":"tibble.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Gains table & lift chart — blr_gains_table","text":"Agresti, . (2007), Introduction Categorical Data Analysis, Second Edition, New York: John Wiley & Sons. Agresti, . (2013), Categorical Data Analysis, Third Edition, New York: John Wiley & Sons. Thomas LC (2009): Consumer Credit Models: Pricing, Profit, Portfolio. Oxford, Oxford Uni-versity Press. Sobehart J, Keenan S, Stein R (2000): Benchmarking Quantitative Default Risk Models: Validation Methodology, Moody’s Investors Service.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gains_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gains table & lift chart — blr_gains_table","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) # gains table blr_gains_table(model) #> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy #> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 #> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 #> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 #> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 #> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 #> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 #> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 #> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 #> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 #> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5 # lift chart k <- blr_gains_table(model) plot(k)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Gini index — blr_gini_index","title":"Gini index — blr_gini_index","text":"Gini index measure inequality developed measure income inequality labour market. predictive model, Gini Index used measuring discriminatory power.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gini index — blr_gini_index","text":"","code":"blr_gini_index(model, data = NULL)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gini index — blr_gini_index","text":"model object class glm. data tibble data.frame.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gini index — blr_gini_index","text":"Gini index.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Gini index — blr_gini_index","text":"Siddiqi N (2006): Credit Risk Scorecards: developing implementing intelligent credit scoring. New Jersey, Wiley. Müller M, Rönz B (2000): Credit Scoring using Semiparametric Methods. : Franke J, Härdle W, Stahl G (Eds.): Measuring Risk Complex Stochastic Systems. New York, Springer-Verlag.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_gini_index.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gini index — blr_gini_index","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_gini_index(model) #> [1] 0.5252134"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_ks_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"KS chart — blr_ks_chart","title":"KS chart — blr_ks_chart","text":"Kolmogorov-Smirnov (KS) statistics used assess predictive power marketing credit risk models. maximum difference cumulative event non-event distribution across score/probability bands. gains table typically across score bands can used find KS model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_ks_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"KS chart — blr_ks_chart","text":"","code":"blr_ks_chart( gains_table, title = \"KS Chart\", yaxis_title = \" \", xaxis_title = \"Cumulative Population %\", ks_line_color = \"black\", print_plot = TRUE )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_ks_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"KS chart — blr_ks_chart","text":"gains_table object class blr_gains_table. title Plot title. yaxis_title Y axis title. xaxis_title X axis title. ks_line_color Color line indicating maximum KS statistic. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_ks_chart.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"KS chart — blr_ks_chart","text":"https://pubmed.ncbi.nlm.nih.gov/843576/","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_ks_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"KS chart — blr_ks_chart","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_ks_chart(gt)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_launch_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Launch shiny app — blr_launch_app","title":"Launch shiny app — blr_launch_app","text":"Launches shiny app interactive model building.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_launch_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Launch shiny app — blr_launch_app","text":"","code":"blr_launch_app()"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_launch_app.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Launch shiny app — blr_launch_app","text":"","code":"if (FALSE) { # \\dontrun{ blr_launch_app() } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":null,"dir":"Reference","previous_headings":"","what":"Model specification error — blr_linktest","title":"Model specification error — blr_linktest","text":"Test model specification error.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model specification error — blr_linktest","text":"","code":"blr_linktest(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model specification error — blr_linktest","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model specification error — blr_linktest","text":"object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Model specification error — blr_linktest","text":"Pregibon, D. 1979. Data analytic methods generalized linear models. PhD diss., University Toronto. Pregibon, D. 1980. Goodness link tests generalized linear models. Tukey, J. W. 1949. One degree freedom non-additivity.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_linktest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model specification error — blr_linktest","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_linktest(model) #> #> Call: #> glm(formula = resp ~ fit + fit2, family = binomial(link = \"logit\"), #> data = newdat) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 0.03569 0.23399 0.153 0.879 #> fit 0.93715 0.20767 4.513 6.4e-06 *** #> fit2 -0.03920 0.08970 -0.437 0.662 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 231.29 on 199 degrees of freedom #> Residual deviance: 160.04 on 197 degrees of freedom #> AIC: 166.04 #> #> Number of Fisher Scoring iterations: 6 #>"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_lorenz_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Lorenz curve — blr_lorenz_curve","title":"Lorenz curve — blr_lorenz_curve","text":"Lorenz curve visual representation inequality. used measure discriminatory power predictive model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_lorenz_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lorenz curve — blr_lorenz_curve","text":"","code":"blr_lorenz_curve( model, data = NULL, title = \"Lorenz Curve\", xaxis_title = \"Cumulative Events %\", yaxis_title = \"Cumulative Non Events %\", diag_line_col = \"red\", lorenz_curve_col = \"blue\", print_plot = TRUE )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_lorenz_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lorenz curve — blr_lorenz_curve","text":"model object class glm. data tibble data.frame. title Plot title. xaxis_title X axis title. yaxis_title Y axis title. diag_line_col Diagonal line color. lorenz_curve_col Color lorenz curve. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_lorenz_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lorenz curve — blr_lorenz_curve","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_lorenz_curve(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_model_fit_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Model fit statistics — blr_model_fit_stats","title":"Model fit statistics — blr_model_fit_stats","text":"Model fit statistics.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_model_fit_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model fit statistics — blr_model_fit_stats","text":"","code":"blr_model_fit_stats(model, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_model_fit_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model fit statistics — blr_model_fit_stats","text":"model object class glm. ... inputs.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_model_fit_stats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Model fit statistics — blr_model_fit_stats","text":"Menard, S. (2000). Coefficients determination multiple logistic regression analysis. American Statistician, 54(1), 17-24. Windmeijer, F. . G. (1995). Goodness--fit measures binary choice models. Econometric Reviews, 14, 101-116. Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). New York: John Wiley & Sons. J. Scott Long & Jeremy Freese, 2000. \"FITSTAT: Stata module compute fit statistics single equation regression models,\" Statistical Software Components S407201, Boston College Department Economics, revised 22 Feb 2001. Freese, Jeremy J. Scott Long. Regression Models Categorical Dependent Variables Using Stata. College Station: Stata Press, 2006. Long, J. Scott. Regression Models Categorical Limited Dependent Variables. Thousand Oaks: Sage Publications, 1997.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_model_fit_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model fit statistics — blr_model_fit_stats","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_model_fit_stats(model) #> Model Fit Statistics #> --------------------------------------------------------------------------------- #> Log-Lik Intercept Only: -115.644 Log-Lik Full Model: -80.118 #> Deviance(196): 160.236 LR(3): 71.052 #> Prob > LR: 0.000 #> MCFadden's R2 0.307 McFadden's Adj R2: 0.273 #> ML (Cox-Snell) R2: 0.299 Cragg-Uhler(Nagelkerke) R2: 0.436 #> McKelvey & Zavoina's R2: 0.518 Efron's R2: 0.330 #> Count R2: 0.810 Adj Count R2: 0.283 #> BIC: 181.430 AIC: 168.236 #> --------------------------------------------------------------------------------- #>"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_multi_model_fit_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi model fit statistics — blr_multi_model_fit_stats","title":"Multi model fit statistics — blr_multi_model_fit_stats","text":"Measures model fit statistics multiple models.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_multi_model_fit_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi model fit statistics — blr_multi_model_fit_stats","text":"","code":"blr_multi_model_fit_stats(model, ...) # Default S3 method blr_multi_model_fit_stats(model, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_multi_model_fit_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi model fit statistics — blr_multi_model_fit_stats","text":"model object class glm. ... Objects class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_multi_model_fit_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi model fit statistics — blr_multi_model_fit_stats","text":"tibble.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_multi_model_fit_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi model fit statistics — blr_multi_model_fit_stats","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) model2 <- glm(honcomp ~ female + read + math, data = hsb2, family = binomial(link = 'logit')) blr_multi_model_fit_stats(model, model2) #> Measures Model 1 Model 2 #> loglik_null Log-Lik Intercept Only -115.644 -115.644 #> loglik_model Log-Lik Full Model -80.118 -75.210 #> m_deviance Deviance 160.236 150.420 #> lr_ratio LR 71.052 80.869 #> lr_pval Prob > LR 0.000 0.000 #> mcfadden MCFadden's R2 0.307 0.350 #> adj_mcfadden McFadden's Adj R2 0.273 0.315 #> m_aic ML (Cox-Snell) R2 168.236 158.420 #> cox_snell Cragg-Uhler(Nagelkerke) R2 0.299 0.333 #> m_bic McKelvey & Zavoina's R2 181.430 171.613 #> mckelvey Efron's R2 0.518 0.523 #> effron Count R2 0.330 0.379 #> nagelkerke Adj Count R2 0.436 0.485 #> count_r2 AIC 0.810 0.830 #> count_adj BIC 0.283 0.358"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_pairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Concordant & discordant pairs — blr_pairs","title":"Concordant & discordant pairs — blr_pairs","text":"Association predicted probabilities observed responses.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_pairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concordant & discordant pairs — blr_pairs","text":"","code":"blr_pairs(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_pairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concordant & discordant pairs — blr_pairs","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_pairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Concordant & discordant pairs — blr_pairs","text":"tibble.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_pairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concordant & discordant pairs — blr_pairs","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_pairs(model) #> pairs concordance discordance tied somers_d gamma tau #> 1 7791 0.856116 0.1424721 0.001411886 0.714653 0.7136439 0.279397 #> c #> 1 0.856822"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"CI Displacement C vs fitted values plot — blr_plot_c_fitted","title":"CI Displacement C vs fitted values plot — blr_plot_c_fitted","text":"Confidence interval displacement diagnostics C vs fitted values plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CI Displacement C vs fitted values plot — blr_plot_c_fitted","text":"","code":"blr_plot_c_fitted( model, point_color = \"blue\", title = \"CI Displacement C vs Fitted Values Plot\", xaxis_title = \"Fitted Values\", yaxis_title = \"CI Displacement C\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CI Displacement C vs fitted values plot — blr_plot_c_fitted","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CI Displacement C vs fitted values plot — blr_plot_c_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_c_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"CI Displacement C vs leverage plot — blr_plot_c_leverage","title":"CI Displacement C vs leverage plot — blr_plot_c_leverage","text":"Confidence interval displacement diagnostics C vs leverage plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CI Displacement C vs leverage plot — blr_plot_c_leverage","text":"","code":"blr_plot_c_leverage( model, point_color = \"blue\", title = \"CI Displacement C vs Leverage Plot\", xaxis_title = \"Leverage\", yaxis_title = \"CI Displacement C\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CI Displacement C vs leverage plot — blr_plot_c_leverage","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_c_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CI Displacement C vs leverage plot — blr_plot_c_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_c_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Deviance vs fitted values plot — blr_plot_deviance_fitted","title":"Deviance vs fitted values plot — blr_plot_deviance_fitted","text":"Deviance vs fitted values plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deviance vs fitted values plot — blr_plot_deviance_fitted","text":"","code":"blr_plot_deviance_fitted( model, point_color = \"blue\", line_color = \"red\", title = \"Deviance Residual vs Fitted Values\", xaxis_title = \"Fitted Values\", yaxis_title = \"Deviance Residual\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deviance vs fitted values plot — blr_plot_deviance_fitted","text":"model object class glm. point_color Color points. line_color Color horizontal line. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deviance vs fitted values plot — blr_plot_deviance_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_deviance_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_residual.html","id":null,"dir":"Reference","previous_headings":"","what":"Deviance residual values — blr_plot_deviance_residual","title":"Deviance residual values — blr_plot_deviance_residual","text":"Deviance residuals plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_residual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deviance residual values — blr_plot_deviance_residual","text":"","code":"blr_plot_deviance_residual( model, point_color = \"blue\", title = \"Deviance Residuals Plot\", xaxis_title = \"id\", yaxis_title = \"Deviance Residuals\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_residual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deviance residual values — blr_plot_deviance_residual","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_deviance_residual.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deviance residual values — blr_plot_deviance_residual","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_deviance_residual(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":null,"dir":"Reference","previous_headings":"","what":"DFBETAs panel — blr_plot_dfbetas_panel","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"Panel plots detect influential observations using DFBETAs.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"","code":"blr_plot_dfbetas_panel(model, print_plot = TRUE)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"model object class glm. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"list; blr_dfbetas_panel returns list tibbles (intercept predictor) observation number DFBETA observations exceed threshold classifying observation outlier/influential observation.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"DFBETA measures difference parameter estimate without influential point. DFBETA data point .e n observations k variables, \\(n * k\\) DFBETAs. general, large values DFBETAS indicate observations influential estimating given parameter. Belsley, Kuh, Welsch recommend 2 general cutoff value indicate influential observations \\(2/\\sqrt(n)\\) size-adjusted cutoff.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"Belsley, David .; Kuh, Edwin; Welsh, Roy E. (1980). Regression Diagnostics: Identifying Influential Data Sources Collinearity. Wiley Series Probability Mathematical Statistics. New York: John Wiley & Sons. pp. ISBN 0-471-05856-4.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_dfbetas_panel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DFBETAs panel — blr_plot_dfbetas_panel","text":"","code":"if (FALSE) { # \\dontrun{ model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_dfbetas_panel(model) } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_c.html","id":null,"dir":"Reference","previous_headings":"","what":"CI Displacement C plot — blr_plot_diag_c","title":"CI Displacement C plot — blr_plot_diag_c","text":"Confidence interval displacement diagnostics C plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_c.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CI Displacement C plot — blr_plot_diag_c","text":"","code":"blr_plot_diag_c( model, point_color = \"blue\", title = \"CI Displacement C Plot\", xaxis_title = \"id\", yaxis_title = \"CI Displacement C\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_c.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CI Displacement C plot — blr_plot_diag_c","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_c.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CI Displacement C plot — blr_plot_diag_c","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_c(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_cbar.html","id":null,"dir":"Reference","previous_headings":"","what":"CI Displacement CBAR plot — blr_plot_diag_cbar","title":"CI Displacement CBAR plot — blr_plot_diag_cbar","text":"Confidence interval displacement diagnostics CBAR plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_cbar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CI Displacement CBAR plot — blr_plot_diag_cbar","text":"","code":"blr_plot_diag_cbar( model, point_color = \"blue\", title = \"CI Displacement CBAR Plot\", xaxis_title = \"id\", yaxis_title = \"CI Displacement CBAR\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_cbar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CI Displacement CBAR plot — blr_plot_diag_cbar","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_cbar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CI Displacement CBAR plot — blr_plot_diag_cbar","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_cbar(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difchisq.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta chisquare plot — blr_plot_diag_difchisq","title":"Delta chisquare plot — blr_plot_diag_difchisq","text":"Diagnostics detecting ill fitted observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difchisq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta chisquare plot — blr_plot_diag_difchisq","text":"","code":"blr_plot_diag_difchisq( model, point_color = \"blue\", title = \"Delta Chisquare Plot\", xaxis_title = \"id\", yaxis_title = \"Delta Chisquare\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difchisq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta chisquare plot — blr_plot_diag_difchisq","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difchisq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta chisquare plot — blr_plot_diag_difchisq","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_difchisq(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difdev.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta deviance plot — blr_plot_diag_difdev","title":"Delta deviance plot — blr_plot_diag_difdev","text":"Diagnostics detecting ill fitted observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difdev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta deviance plot — blr_plot_diag_difdev","text":"","code":"blr_plot_diag_difdev( model, point_color = \"blue\", title = \"Delta Deviance Plot\", xaxis_title = \"id\", yaxis_title = \"Delta Deviance\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difdev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta deviance plot — blr_plot_diag_difdev","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_difdev.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta deviance plot — blr_plot_diag_difdev","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_difdev(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitted values diagnostics plot — blr_plot_diag_fit","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"Diagnostic plots fitted values.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"","code":"blr_plot_diag_fit(model, print_plot = TRUE)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"model object class glm. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"panel diagnostic plots fitted values.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications. Cook, R. D. Weisberg, S. (1982), Residuals Influence Regression, New York: Chapman & Hall.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitted values diagnostics plot — blr_plot_diag_fit","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_fit(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":null,"dir":"Reference","previous_headings":"","what":"Influence diagnostics plot — blr_plot_diag_influence","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"Reisudal diagnostic plots detecting influential observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"","code":"blr_plot_diag_influence(model, print_plot = TRUE)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"model object class glm. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"panel influence diagnostic plots.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications. Cook, R. D. Weisberg, S. (1982), Residuals Influence Regression, New York: Chapman & Hall.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_influence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Influence diagnostics plot — blr_plot_diag_influence","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_influence(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Leverage diagnostics plot — blr_plot_diag_leverage","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"Diagnostic plots leverage.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"","code":"blr_plot_diag_leverage(model, print_plot = TRUE)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"model object class glm. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"panel diagnostic plots leverage.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"Fox, John (1991), Regression Diagnostics. Newbury Park, CA: Sage Publications. Cook, R. D. Weisberg, S. (1982), Residuals Influence Regression, New York: Chapman & Hall.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_diag_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leverage diagnostics plot — blr_plot_diag_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_diag_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta chi square vs fitted values plot — blr_plot_difchisq_fitted","title":"Delta chi square vs fitted values plot — blr_plot_difchisq_fitted","text":"Delta Chi Square vs fitted values plot detecting ill fitted observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta chi square vs fitted values plot — blr_plot_difchisq_fitted","text":"","code":"blr_plot_difchisq_fitted( model, point_color = \"blue\", title = \"Delta Chi Square vs Fitted Values Plot\", xaxis_title = \"Fitted Values\", yaxis_title = \"Delta Chi Square\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta chi square vs fitted values plot — blr_plot_difchisq_fitted","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta chi square vs fitted values plot — blr_plot_difchisq_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_difchisq_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta chi square vs leverage plot — blr_plot_difchisq_leverage","title":"Delta chi square vs leverage plot — blr_plot_difchisq_leverage","text":"Delta chi square vs leverage plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta chi square vs leverage plot — blr_plot_difchisq_leverage","text":"","code":"blr_plot_difchisq_leverage( model, point_color = \"blue\", title = \"Delta Chi Square vs Leverage Plot\", xaxis_title = \"Leverage\", yaxis_title = \"Delta Chi Square\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta chi square vs leverage plot — blr_plot_difchisq_leverage","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difchisq_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta chi square vs leverage plot — blr_plot_difchisq_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_difchisq_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta deviance vs fitted values plot — blr_plot_difdev_fitted","title":"Delta deviance vs fitted values plot — blr_plot_difdev_fitted","text":"Delta deviance vs fitted values plot detecting ill fitted observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta deviance vs fitted values plot — blr_plot_difdev_fitted","text":"","code":"blr_plot_difdev_fitted( model, point_color = \"blue\", title = \"Delta Deviance vs Fitted Values Plot\", xaxis_title = \"Fitted Values\", yaxis_title = \"Delta Deviance\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta deviance vs fitted values plot — blr_plot_difdev_fitted","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta deviance vs fitted values plot — blr_plot_difdev_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_difdev_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Delta deviance vs leverage plot — blr_plot_difdev_leverage","title":"Delta deviance vs leverage plot — blr_plot_difdev_leverage","text":"Delta deviance vs leverage plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Delta deviance vs leverage plot — blr_plot_difdev_leverage","text":"","code":"blr_plot_difdev_leverage( model, point_color = \"blue\", title = \"Delta Deviance vs Leverage Plot\", xaxis_title = \"Leverage\", yaxis_title = \"Delta Deviance\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Delta deviance vs leverage plot — blr_plot_difdev_leverage","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_difdev_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Delta deviance vs leverage plot — blr_plot_difdev_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_difdev_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_fitted_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitted values vs leverage plot — blr_plot_fitted_leverage","title":"Fitted values vs leverage plot — blr_plot_fitted_leverage","text":"Fitted values vs leverage plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_fitted_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitted values vs leverage plot — blr_plot_fitted_leverage","text":"","code":"blr_plot_fitted_leverage( model, point_color = \"blue\", title = \"Fitted Values vs Leverage Plot\", xaxis_title = \"Leverage\", yaxis_title = \"Fitted Values\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_fitted_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitted values vs leverage plot — blr_plot_fitted_leverage","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_fitted_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitted values vs leverage plot — blr_plot_fitted_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_fitted_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage.html","id":null,"dir":"Reference","previous_headings":"","what":"Leverage plot — blr_plot_leverage","title":"Leverage plot — blr_plot_leverage","text":"Leverage plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leverage plot — blr_plot_leverage","text":"","code":"blr_plot_leverage( model, point_color = \"blue\", title = \"Leverage Plot\", xaxis_title = \"id\", yaxis_title = \"Leverage\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leverage plot — blr_plot_leverage","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leverage plot — blr_plot_leverage","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_leverage(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Leverage vs fitted values plot — blr_plot_leverage_fitted","title":"Leverage vs fitted values plot — blr_plot_leverage_fitted","text":"Leverage vs fitted values plot","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leverage vs fitted values plot — blr_plot_leverage_fitted","text":"","code":"blr_plot_leverage_fitted( model, point_color = \"blue\", title = \"Leverage vs Fitted Values\", xaxis_title = \"Fitted Values\", yaxis_title = \"Leverage\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leverage vs fitted values plot — blr_plot_leverage_fitted","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_leverage_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leverage vs fitted values plot — blr_plot_leverage_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_leverage_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_pearson_residual.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual values plot — blr_plot_pearson_residual","title":"Residual values plot — blr_plot_pearson_residual","text":"Standardised pearson residuals plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_pearson_residual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual values plot — blr_plot_pearson_residual","text":"","code":"blr_plot_pearson_residual( model, point_color = \"blue\", title = \"Standardized Pearson Residuals\", xaxis_title = \"id\", yaxis_title = \"Standardized Pearson Residuals\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_pearson_residual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual values plot — blr_plot_pearson_residual","text":"model object class glm. point_color Color points. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_pearson_residual.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Residual values plot — blr_plot_pearson_residual","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_pearson_residual(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_residual_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual vs fitted values plot — blr_plot_residual_fitted","title":"Residual vs fitted values plot — blr_plot_residual_fitted","text":"Residual vs fitted values plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_residual_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual vs fitted values plot — blr_plot_residual_fitted","text":"","code":"blr_plot_residual_fitted( model, point_color = \"blue\", line_color = \"red\", title = \"Standardized Pearson Residual vs Fitted Values\", xaxis_title = \"Fitted Values\", yaxis_title = \"Standardized Pearson Residual\" )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_residual_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual vs fitted values plot — blr_plot_residual_fitted","text":"model object class glm. point_color Color points. line_color Color horizontal line. title Title plot. xaxis_title X axis label. yaxis_title Y axis label.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_plot_residual_fitted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Residual vs fitted values plot — blr_plot_residual_fitted","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_plot_residual_fitted(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_dcrate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Decile capture rate data — blr_prep_dcrate_data","title":"Decile capture rate data — blr_prep_dcrate_data","text":"Data generating decile capture rate.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_dcrate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decile capture rate data — blr_prep_dcrate_data","text":"","code":"blr_prep_dcrate_data(gains_table)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_dcrate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decile capture rate data — blr_prep_dcrate_data","text":"gains_table object clas blr_gains_table","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_dcrate_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decile capture rate data — blr_prep_dcrate_data","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_prep_dcrate_data(gt) #> decile total 1 decile_mean #> 1 1 20 14 0.70 #> 2 2 20 13 0.65 #> 3 3 20 10 0.50 #> 4 4 20 7 0.35 #> 5 5 20 3 0.15 #> 6 6 20 3 0.15 #> 7 7 20 1 0.05 #> 8 8 20 2 0.10 #> 9 9 20 0 0.00 #> 10 10 20 0 0.00"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_kschart_data.html","id":null,"dir":"Reference","previous_headings":"","what":"KS Chart data — blr_prep_kschart_data","title":"KS Chart data — blr_prep_kschart_data","text":"Data generating KS chart.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_kschart_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"KS Chart data — blr_prep_kschart_data","text":"","code":"blr_prep_kschart_data(gains_table) blr_prep_kschart_line(gains_table) blr_prep_ksannotate_y(ks_line) blr_prep_kschart_stat(ks_line) blr_prep_ksannotate_x(ks_line)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_kschart_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"KS Chart data — blr_prep_kschart_data","text":"gains_table object clas blr_gains_table. ks_line Overall conversion rate.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_kschart_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"KS Chart data — blr_prep_kschart_data","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_prep_kschart_data(gt) #> cum_total_per cum_1s_per cum_0s_per #> 1 0.0 0.0000000 0.00000000 #> 2 0.1 0.2641509 0.04081633 #> 3 0.2 0.5094340 0.08843537 #> 4 0.3 0.6981132 0.15646259 #> 5 0.4 0.8301887 0.24489796 #> 6 0.5 0.8867925 0.36054422 #> 7 0.6 0.9433962 0.47619048 #> 8 0.7 0.9622642 0.60544218 #> 9 0.8 1.0000000 0.72789116 #> 10 0.9 1.0000000 0.86394558 #> 11 1.0 1.0000000 1.00000000 ks_line <- blr_prep_kschart_line(gt) blr_prep_kschart_stat(ks_line) #> [1] 59 blr_prep_ksannotate_y(ks_line) #> [1] 0.5375433 blr_prep_ksannotate_x(ks_line) #> [1] 0.5"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lchart_gmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Lift Chart data — blr_prep_lchart_gmean","title":"Lift Chart data — blr_prep_lchart_gmean","text":"Data generating lift chart.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lchart_gmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lift Chart data — blr_prep_lchart_gmean","text":"","code":"blr_prep_lchart_gmean(gains_table) blr_prep_lchart_data(gains_table, global_mean)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lchart_gmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lift Chart data — blr_prep_lchart_gmean","text":"gains_table object clas blr_gains_table. global_mean Overall conversion rate.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lchart_gmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lift Chart data — blr_prep_lchart_gmean","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) globalmean <- blr_prep_lchart_gmean(gt) blr_prep_lchart_data(gt, globalmean) #> decile total 1 decile_mean d_by_g_mean #> 1 1 20 14 0.70 2.6415094 #> 2 2 20 13 0.65 2.4528302 #> 3 3 20 10 0.50 1.8867925 #> 4 4 20 7 0.35 1.3207547 #> 5 5 20 3 0.15 0.5660377 #> 6 6 20 3 0.15 0.5660377 #> 7 7 20 1 0.05 0.1886792 #> 8 8 20 2 0.10 0.3773585 #> 9 9 20 0 0.00 0.0000000 #> 10 10 20 0 0.00 0.0000000"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lorenz_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Lorenz curve data — blr_prep_lorenz_data","title":"Lorenz curve data — blr_prep_lorenz_data","text":"Data generating Lorenz curve.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lorenz_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lorenz curve data — blr_prep_lorenz_data","text":"","code":"blr_prep_lorenz_data(model, data = NULL, test_data = FALSE)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lorenz_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lorenz curve data — blr_prep_lorenz_data","text":"model object class glm. data tibble data.frame. test_data Logical; TRUE data test data FALSE training data.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_lorenz_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lorenz curve data — blr_prep_lorenz_data","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) data <- model$data blr_prep_lorenz_data(model, data, FALSE) #> cum_0s_per cum_1s_per #> 1 0.00000000 0.0000000 #> 2 0.04081633 0.2641509 #> 3 0.08843537 0.5094340 #> 4 0.15646259 0.6981132 #> 5 0.24489796 0.8301887 #> 6 0.36054422 0.8867925 #> 7 0.47619048 0.9433962 #> 8 0.60544218 0.9622642 #> 9 0.72789116 1.0000000 #> 10 0.86394558 1.0000000 #> 11 1.00000000 1.0000000 #> 12 1.00000000 1.0000000"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_roc_data.html","id":null,"dir":"Reference","previous_headings":"","what":"ROC curve data — blr_prep_roc_data","title":"ROC curve data — blr_prep_roc_data","text":"Data generating ROC curve.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_roc_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ROC curve data — blr_prep_roc_data","text":"","code":"blr_prep_roc_data(gains_table)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_roc_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ROC curve data — blr_prep_roc_data","text":"gains_table object clas blr_gains_table","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_prep_roc_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ROC curve data — blr_prep_roc_data","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_prep_roc_data(gt) #> sensitivity specificity sensitivity_per 1 - specificity #> 1 NA NA 0.0000000 0.00000000 #> 2 26.41509 95.91837 0.2641509 0.04081633 #> 3 50.94340 91.15646 0.5094340 0.08843537 #> 4 69.81132 84.35374 0.6981132 0.15646259 #> 5 83.01887 75.51020 0.8301887 0.24489796 #> 6 88.67925 63.94558 0.8867925 0.36054422 #> 7 94.33962 52.38095 0.9433962 0.47619048 #> 8 96.22642 39.45578 0.9622642 0.60544218 #> 9 100.00000 27.21088 1.0000000 0.72789116 #> 10 100.00000 13.60544 1.0000000 0.86394558 #> 11 100.00000 0.00000 1.0000000 1.00000000"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_regress.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary logistic regression — blr_regress","title":"Binary logistic regression — blr_regress","text":"Binary logistic regression.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_regress.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary logistic regression — blr_regress","text":"","code":"blr_regress(object, ...) # S3 method for class 'glm' blr_regress(object, odd_conf_limit = FALSE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_regress.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary logistic regression — blr_regress","text":"object object class \"formula\" (one can coerced class): symbolic description model fitted class glm. ... inputs. odd_conf_limit TRUE, odds ratio confidence limts displayed.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_regress.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary logistic regression — blr_regress","text":"","code":"# using formula blr_regress(object = honcomp ~ female + read + science, data = hsb2) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #> # using a model built with glm model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_regress(model) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #> # odds ratio estimates blr_regress(model, odd_conf_limit = TRUE) #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Odds Ratio Estimates #> --------------------------------------------------------- #> Effects Estimate 95% Wald Conf. Limit #> --------------------------------------------------------- #> female1 4.4039 1.8955 11.0521 #> read 1.1091 1.0569 1.1699 #> science 1.0994 1.0377 1.1702 #> --------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #>"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_residual_diagnostics.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual diagnostics — blr_residual_diagnostics","title":"Residual diagnostics — blr_residual_diagnostics","text":"Diagnostics confidence interval displacement detecting ill fitted observations.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_residual_diagnostics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual diagnostics — blr_residual_diagnostics","text":"","code":"blr_residual_diagnostics(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_residual_diagnostics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual diagnostics — blr_residual_diagnostics","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_residual_diagnostics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual diagnostics — blr_residual_diagnostics","text":"C, CBAR, DIFDEV DIFCHISQ.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_residual_diagnostics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Residual diagnostics — blr_residual_diagnostics","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_residual_diagnostics(model) #> c cbar difdev difchisq #> 1 1.714775e-03 1.682909e-03 0.175200955 0.090559751 #> 2 1.429214e-01 1.394315e-01 3.998937227 5.710209661 #> 3 9.959732e-04 9.813000e-04 0.130007342 0.066607468 #> 4 9.023852e-03 8.761106e-03 0.536722606 0.300894248 #> 5 5.980764e-04 5.917929e-04 0.110220460 0.056328100 #> 6 2.876007e-03 2.801553e-03 0.208574176 0.108218735 #> 7 8.995744e-04 8.891766e-04 0.149175691 0.076927702 #> 8 9.902361e-06 9.877010e-06 0.007711173 0.003858052 #> 9 1.219551e-02 1.189404e-02 0.800918692 0.481155824 #> 10 2.163331e-03 2.125096e-03 0.229423218 0.120235791 #> 11 4.832076e-03 4.727697e-03 0.401357308 0.218861551 #> 12 6.191650e-02 6.043716e-02 2.609131129 2.529552930 #> 13 2.995002e-02 2.852142e-02 0.975083831 0.597941207 #> 14 1.048892e-01 1.034023e-01 4.369886254 7.294114623 #> 15 2.555955e-05 2.544456e-05 0.011305342 0.005655812 #> 16 1.890339e-04 1.876253e-04 0.049929085 0.025180243 #> 17 3.927621e-04 3.891513e-04 0.083321991 0.042330035 #> 18 4.470660e-03 4.394080e-03 0.461923138 0.256523888 #> 19 8.222657e-02 7.856862e-02 2.147893802 1.766130095 #> 20 1.922340e-03 1.894221e-03 0.245647296 0.129496817 #> 21 3.360228e-02 3.254494e-02 1.465662476 1.034273716 #> 22 3.070469e-02 2.919663e-02 0.971586410 0.594457116 #> 23 1.187856e-03 1.172889e-03 0.179275765 0.093083875 #> 24 1.024315e-02 1.002336e-02 0.779442376 0.467132920 #> 25 1.666718e-05 1.661460e-05 0.010522075 0.005266506 #> 26 9.902361e-06 9.877010e-06 0.007711173 0.003858052 #> 27 3.868987e-02 3.760510e-02 1.754720088 1.341233680 #> 28 1.397582e-03 1.376421e-03 0.175507244 0.090907237 #> 29 5.883201e-05 5.851604e-05 0.021732193 0.010895452 #> 30 1.538551e-03 1.518165e-03 0.218615492 0.114575242 #> 31 1.206777e-04 1.199131e-04 0.037621469 0.018926550 #> 32 1.582197e-01 1.510534e-01 3.149359572 3.334983697 #> 33 2.790231e-02 2.700378e-02 1.254920166 0.838561702 #> 34 1.832472e-02 1.690403e-02 0.414238978 0.218036836 #> 35 4.199876e-03 4.115064e-03 0.375701229 0.203778702 #> 36 4.902223e-02 4.797035e-02 2.417385169 2.235629715 #> 37 6.184782e-02 5.972581e-02 2.102207404 1.740757786 #> 38 2.790231e-02 2.700378e-02 1.254920166 0.838561702 #> 39 7.221982e-05 7.175699e-05 0.022341692 0.011196792 #> 40 2.371239e-05 2.362595e-05 0.012944686 0.006481368 #> 41 1.484749e-04 1.473695e-04 0.039352793 0.019794657 #> 42 3.430254e-03 3.374692e-03 0.382426519 0.208346373 #> 43 1.538551e-03 1.518165e-03 0.218615492 0.114575242 #> 44 1.890339e-04 1.876253e-04 0.049929085 0.025180243 #> 45 3.282110e-02 3.187881e-02 1.538424442 1.110378773 #> 46 3.133334e-02 2.980554e-02 0.993508264 0.611278393 #> 47 5.583226e-04 5.524581e-04 0.103098442 0.052595923 #> 48 2.821714e-02 2.740703e-02 1.378342821 0.954620784 #> 49 5.980764e-04 5.917929e-04 0.110220460 0.056328100 #> 50 1.714775e-03 1.682909e-03 0.175200955 0.090559751 #> 51 1.007565e-01 9.926312e-02 4.216014793 6.697126795 #> 52 7.587586e-05 7.546277e-05 0.027607615 0.013860744 #> 53 2.523756e-04 2.503371e-04 0.061302700 0.030993042 #> 54 1.005310e-02 9.782114e-03 0.631353787 0.362895817 #> 55 1.049232e-02 1.010920e-02 0.500933885 0.276855002 #> 56 3.884597e-06 3.876617e-06 0.003774610 0.001887141 #> 57 7.089019e-06 7.072193e-06 0.005957749 0.002979759 #> 58 5.314174e-04 5.251688e-04 0.087936379 0.044663202 #> 59 1.219551e-02 1.189404e-02 0.800918692 0.481155824 #> 60 2.250623e-04 2.231714e-04 0.052660204 0.026562307 #> 61 1.937427e-03 1.907634e-03 0.235987514 0.124050481 #> 62 2.447935e-01 2.312153e-01 3.612373822 4.168455201 #> 63 3.099590e-05 3.085479e-05 0.013539849 0.006777256 #> 64 6.322787e-06 6.309041e-06 0.005801949 0.002902014 #> 65 1.324601e-04 1.313238e-04 0.030518715 0.015308706 #> 66 2.523756e-04 2.503371e-04 0.061302700 0.030993042 #> 67 5.521616e-03 5.408598e-03 0.475398199 0.264241258 #> 68 1.484749e-04 1.473695e-04 0.039352793 0.019794657 #> 69 2.843505e-02 2.759905e-02 1.362233451 0.938735201 #> 70 3.258439e-04 3.226311e-04 0.064728136 0.032721649 #> 71 5.883201e-05 5.851604e-05 0.021732193 0.010895452 #> 72 7.100426e-03 6.959611e-03 0.610177412 0.350928915 #> 73 2.223940e-02 2.127829e-02 0.828148303 0.492362786 #> 74 1.711199e-01 1.636562e-01 3.349721185 3.752139610 #> 75 3.430254e-03 3.374692e-03 0.382426519 0.208346373 #> 76 2.115485e-04 2.098617e-04 0.052173679 0.026319642 #> 77 1.778370e-01 1.751576e-01 5.295761642 11.625223156 #> 78 4.554600e-02 4.401450e-02 1.736019169 1.308970340 #> 79 5.923198e-06 5.910812e-06 0.005651206 0.002826627 #> 80 1.484749e-04 1.473695e-04 0.039352793 0.019794657 #> 81 1.005310e-02 9.782114e-03 0.631353787 0.362895817 #> 82 6.867317e-04 6.747790e-04 0.076771905 0.038768758 #> 83 6.184782e-02 5.972581e-02 2.102207404 1.740757786 #> 84 3.683128e-02 3.586186e-02 1.770358544 1.362493836 #> 85 8.739224e-04 8.603094e-04 0.108422252 0.055230101 #> 86 7.733278e-02 7.545805e-02 2.935899226 3.112646806 #> 87 1.199785e-03 1.184777e-03 0.182277117 0.094717594 #> 88 1.568983e-05 1.564199e-05 0.010248659 0.005129580 #> 89 1.040428e-04 1.031947e-04 0.025264629 0.012659537 #> 90 5.648486e-03 5.535976e-03 0.497125122 0.277928055 #> 91 2.296646e-01 2.269371e-01 6.278388092 19.108468289 #> 92 3.360228e-02 3.254494e-02 1.465662476 1.034273716 #> 93 2.507119e-03 2.448154e-03 0.200719009 0.104092427 #> 94 4.620611e-04 4.574798e-04 0.090692860 0.046140576 #> 95 1.279493e-03 1.263815e-03 0.197703474 0.103142180 #> 96 5.656904e-02 5.404523e-02 1.663597494 1.211374960 #> 97 2.249922e-03 2.200995e-03 0.195221725 0.101213040 #> 98 5.683130e-03 5.505435e-03 0.330657695 0.176077827 #> 99 1.101009e-04 1.093947e-04 0.033936548 0.017056493 #> 100 9.143496e-03 8.924746e-03 0.645164254 0.373044051 #> 101 2.507119e-03 2.448154e-03 0.200719009 0.104092427 #> 102 7.065249e-02 6.554781e-02 1.382022263 0.907229749 #> 103 5.496769e-04 5.417171e-04 0.074013559 0.037409012 #> 104 3.606158e-02 3.480992e-02 1.437638887 1.002911024 #> 105 2.946148e-02 2.853544e-02 1.331287766 0.907840313 #> 106 5.676501e-03 5.522539e-03 0.377058254 0.203613395 #> 107 3.826369e-02 3.727759e-02 1.842392766 1.446484548 #> 108 9.579393e-04 9.427774e-04 0.116711853 0.059565092 #> 109 2.558035e-03 2.523557e-03 0.346142203 0.187228311 #> 110 6.989810e-03 6.772784e-03 0.402548738 0.218132880 #> 111 3.907660e-02 3.809320e-02 1.897852151 1.513681841 #> 112 5.546298e-04 5.483299e-04 0.094862141 0.048273447 #> 113 5.299547e-02 4.993601e-02 1.315205267 0.864984721 #> 114 7.541574e-03 7.336359e-03 0.486192864 0.269608174 #> 115 1.021654e-02 9.796266e-03 0.438780016 0.238142198 #> 116 2.294176e-04 2.275486e-04 0.055328822 0.027930516 #> 117 4.649203e-03 4.543908e-03 0.370955406 0.200632248 #> 118 5.889263e-03 5.700167e-03 0.333349523 0.177527665 #> 119 1.501761e-02 1.469141e-02 1.052883879 0.676357480 #> 120 3.333480e-04 3.302306e-04 0.069748542 0.035312784 #> 121 1.926803e-02 1.877147e-02 1.119684321 0.728390444 #> 122 3.469276e-03 3.422783e-03 0.458987480 0.255407134 #> 123 1.706084e-03 1.674517e-03 0.175082379 0.090500084 #> 124 2.023481e-03 1.997373e-03 0.290115157 0.154804754 #> 125 3.696036e-01 3.642264e-01 6.950796928 25.035516433 #> 126 1.838113e-02 1.802134e-02 1.330233919 0.920702469 #> 127 2.330179e-03 2.293319e-03 0.273338950 0.144976356 #> 128 1.215048e-03 1.197280e-03 0.158677951 0.081875583 #> 129 2.763419e-03 2.724278e-03 0.354973148 0.192339395 #> 130 2.783763e-05 2.772063e-05 0.013175835 0.006595622 #> 131 1.039078e-02 1.019111e-02 0.864222041 0.530342453 #> 132 2.214178e-02 2.178123e-02 1.729127087 1.337620342 #> 133 3.552271e-02 3.367987e-02 1.045504661 0.649215871 #> 134 1.019456e-03 1.007207e-03 0.162085818 0.083830337 #> 135 2.219997e-02 2.119220e-02 0.793356119 0.466837321 #> 136 9.896093e-04 9.720041e-04 0.107415754 0.054637523 #> 137 1.163020e-02 1.130601e-02 0.695134995 0.405593537 #> 138 1.217273e-03 1.201953e-03 0.183728947 0.095501951 #> 139 3.521794e-04 3.489574e-04 0.075228440 0.038142864 #> 140 3.359504e-03 3.297549e-03 0.332778538 0.178807876 #> 141 1.033954e-04 1.026746e-04 0.029347552 0.014729117 #> 142 1.626688e-03 1.603332e-03 0.213484010 0.111669095 #> 143 9.542028e-04 9.406140e-04 0.128917831 0.066049834 #> 144 1.418413e-03 1.399681e-03 0.203004768 0.105986475 #> 145 5.989086e-03 5.904450e-03 0.705683222 0.417816515 #> 146 2.023481e-03 1.997373e-03 0.290115157 0.154804754 #> 147 4.364463e-02 4.289970e-02 2.574734358 2.513440758 #> 148 9.098518e-04 8.993024e-04 0.150363722 0.077561516 #> 149 1.035674e-03 1.021108e-03 0.141267367 0.072604021 #> 150 4.072810e-04 4.023678e-04 0.066034991 0.033354067 #> 151 1.213217e-03 1.196667e-03 0.169461679 0.087721966 #> 152 6.738631e-02 6.568886e-02 2.660468673 2.607750122 #> 153 1.783288e-03 1.757971e-03 0.235429953 0.123830352 #> 154 4.078075e-03 4.012484e-03 0.450196915 0.249471329 #> 155 1.798284e-02 1.763743e-02 1.327136586 0.918244408 #> 156 9.586315e-02 9.257885e-02 2.750869718 2.702220617 #> 157 6.920782e-03 6.818132e-03 0.765144454 0.459685870 #> 158 1.019456e-03 1.007207e-03 0.162085818 0.083830337 #> 159 5.468997e-04 5.412187e-04 0.102147214 0.052101738 #> 160 6.956322e-03 6.783894e-03 0.491955607 0.273683670 #> 161 6.768584e-02 6.659719e-02 3.367956058 4.140599894 #> 162 1.068424e-02 1.049175e-02 0.931617433 0.582360803 #> 163 9.161703e-02 8.684071e-02 2.085772458 1.665733364 #> 164 1.794676e-02 1.749512e-02 1.079052614 0.695190763 #> 165 8.183569e-03 8.053310e-03 0.829246770 0.505948870 #> 166 6.112573e-04 6.042976e-04 0.104062462 0.053074637 #> 167 2.691798e-02 2.597419e-02 1.143807429 0.740814692 #> 168 7.248779e-04 7.164235e-04 0.119983373 0.061425852 #> 169 2.032750e-02 1.999351e-02 1.620327724 1.216833025 #> 170 2.470070e-02 2.429277e-02 1.843798016 1.470963397 #> 171 1.626688e-03 1.603332e-03 0.213484010 0.111669095 #> 172 1.832056e-03 1.808523e-03 0.265475697 0.140796573 #> 173 1.628309e-04 1.613977e-04 0.036505512 0.018336703 #> 174 5.031675e-02 4.911828e-02 2.308866824 2.062197192 #> 175 3.718687e-04 3.677289e-04 0.065376651 0.033032492 #> 176 1.932064e-02 1.881967e-02 1.116748565 0.725807280 #> 177 1.580237e-02 1.535157e-02 0.881118321 0.538126313 #> 178 2.184174e-03 2.141404e-03 0.209906333 0.109356853 #> 179 1.353245e-02 1.320813e-02 0.895192843 0.551116791 #> 180 3.295260e-02 3.237871e-02 2.147516973 1.859195000 #> 181 3.826369e-02 3.727759e-02 1.842392766 1.446484548 #> 182 5.546298e-04 5.483299e-04 0.094862141 0.048273447 #> 183 4.059106e-02 3.897641e-02 1.420184895 0.979840258 #> 184 3.989745e-02 3.924613e-02 2.506277005 2.404079826 #> 185 6.050690e-02 5.680204e-02 1.391329501 0.927678395 #> 186 1.042797e-02 1.016529e-02 0.690776927 0.403546839 #> 187 5.989086e-03 5.904450e-03 0.705683222 0.417816515 #> 188 1.213217e-03 1.196667e-03 0.169461679 0.087721966 #> 189 1.666544e-02 1.625457e-02 1.034466544 0.659304844 #> 190 5.036356e-03 4.962806e-03 0.591189342 0.339831218 #> 191 5.468997e-04 5.412187e-04 0.102147214 0.052101738 #> 192 8.813239e-04 8.685670e-04 0.117465633 0.060005835 #> 193 7.150669e-03 7.028620e-03 0.698578733 0.411798254 #> 194 1.897529e-04 1.881278e-04 0.043648756 0.021966086 #> 195 7.498085e-02 7.397022e-02 3.841734514 5.488016022 #> 196 5.676501e-03 5.522539e-03 0.377058254 0.203613395 #> 197 2.848621e-04 2.821357e-04 0.058393203 0.029477645 #> 198 1.419097e-02 1.395780e-02 1.248919982 0.849491713 #> 199 2.255815e-02 2.211660e-02 1.543189126 1.129927516 #> 200 2.267186e-02 2.204654e-02 1.204840140 0.799328652"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_roc_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"ROC curve — blr_roc_curve","title":"ROC curve — blr_roc_curve","text":"Receiver operating characteristic curve (ROC) curve used assessing accuracy model classification.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_roc_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ROC curve — blr_roc_curve","text":"","code":"blr_roc_curve( gains_table, title = \"ROC Curve\", xaxis_title = \"1 - Specificity\", yaxis_title = \"Sensitivity\", roc_curve_col = \"blue\", diag_line_col = \"red\", point_shape = 18, point_fill = \"blue\", point_color = \"blue\", plot_title_justify = 0.5, print_plot = TRUE )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_roc_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ROC curve — blr_roc_curve","text":"gains_table object class blr_gains_table. title Plot title. xaxis_title X axis title. yaxis_title Y axis title. roc_curve_col Color roc curve. diag_line_col Diagonal line color. point_shape Shape points roc curve. point_fill Fill points roc curve. point_color Color points roc curve. plot_title_justify Horizontal justification plot title. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_roc_curve.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ROC curve — blr_roc_curve","text":"Agresti, . (2007), Introduction Categorical Data Analysis, Second Edition, New York: John Wiley & Sons. Hosmer, D. W., Jr. Lemeshow, S. (2000), Applied Logistic Regression, 2nd Edition, New York: John Wiley & Sons. Siddiqi N (2006): Credit Risk Scorecards: developing implementing intelligent credit scoring. New Jersey, Wiley. Thomas LC, Edelman DB, Crook JN (2002): Credit Scoring Applications. Philadelphia, SIAM Monographs Mathematical Modeling Computation.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_roc_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ROC curve — blr_roc_curve","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) k <- blr_gains_table(model) blr_roc_curve(k)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_adj_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjusted count R2 — blr_rsq_adj_count","title":"Adjusted count R2 — blr_rsq_adj_count","text":"Adjusted count r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_adj_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjusted count R2 — blr_rsq_adj_count","text":"","code":"blr_rsq_adj_count(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_adj_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjusted count R2 — blr_rsq_adj_count","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_adj_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjusted count R2 — blr_rsq_adj_count","text":"Adjusted count r-squared.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_adj_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjusted count R2 — blr_rsq_adj_count","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_adj_count(model) #> [1] 0.2830189"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count R2 — blr_rsq_count","title":"Count R2 — blr_rsq_count","text":"Count r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count R2 — blr_rsq_count","text":"","code":"blr_rsq_count(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count R2 — blr_rsq_count","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count R2 — blr_rsq_count","text":"Count r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count R2 — blr_rsq_count","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_count(model) #> [1] 0.81"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox Snell R2 — blr_rsq_cox_snell","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"Cox Snell pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"","code":"blr_rsq_cox_snell(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"Cox Snell pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"Cox, D. R., & Snell, E. J. (1989). analysis binary data (2nd ed.). London: Chapman Hall. Maddala, G. S. (1983). Limited dependent qualitative variables economics. New York: Cambridge Press.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_cox_snell.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox Snell R2 — blr_rsq_cox_snell","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_cox_snell(model) #> [1] 0.2990105"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":null,"dir":"Reference","previous_headings":"","what":"Effron R2 — blr_rsq_effron","title":"Effron R2 — blr_rsq_effron","text":"Effron pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Effron R2 — blr_rsq_effron","text":"","code":"blr_rsq_effron(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Effron R2 — blr_rsq_effron","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Effron R2 — blr_rsq_effron","text":"Effron pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Effron R2 — blr_rsq_effron","text":"Efron, B. (1978). Regression ANOVA zero-one data: Measures residual variation. Journal American Statistical Association, 73, 113-121.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_effron.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Effron R2 — blr_rsq_effron","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_effron(model) #> [1] 0.3304513"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":null,"dir":"Reference","previous_headings":"","what":"McFadden's R2 — blr_rsq_mcfadden","title":"McFadden's R2 — blr_rsq_mcfadden","text":"McFadden's pseudo r-squared model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McFadden's R2 — blr_rsq_mcfadden","text":"","code":"blr_rsq_mcfadden(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"McFadden's R2 — blr_rsq_mcfadden","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"McFadden's R2 — blr_rsq_mcfadden","text":"McFadden's r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"McFadden's R2 — blr_rsq_mcfadden","text":"https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"McFadden's R2 — blr_rsq_mcfadden","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_mcfadden(model) #> [1] 0.3072023"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"McFadden's adjusted pseudo r-squared model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"","code":"blr_rsq_mcfadden_adj(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"McFadden's adjusted r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mcfadden_adj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"McFadden's adjusted R2 — blr_rsq_mcfadden_adj","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_mcfadden_adj(model) #> [1] 0.2726135"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":null,"dir":"Reference","previous_headings":"","what":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"McKelvey Zavoina pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"","code":"blr_rsq_mckelvey_zavoina(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"McKelvey, R. D., & Zavoina, W. (1975). statistical model analysis ordinal level dependent variables. Journal Mathematical Sociology, 4, 103-12.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_mckelvey_zavoina.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"McKelvey Zavoina R2 — blr_rsq_mckelvey_zavoina","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_mckelvey_zavoina(model) #> [1] 0.5177764"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":null,"dir":"Reference","previous_headings":"","what":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"","code":"blr_rsq_nagelkerke(model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"model object class glm.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"Cragg-Uhler (Nagelkerke) R2 pseudo r-squared.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"Cragg, S. G., & Uhler, R. (1970). demand automobiles. Canadian Journal Economics, 3, 386-406. Maddala, G. S. (1983). Limited dependent qualitative variables economics. New York: Cambridge Press. Nagelkerke, N. (1991). note general definition coefficient determination.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_rsq_nagelkerke.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cragg-Uhler (Nagelkerke) R2 — blr_rsq_nagelkerke","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_rsq_nagelkerke(model) #> [1] 0.4362587"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment.html","id":null,"dir":"Reference","previous_headings":"","what":"Event rate — blr_segment","title":"Event rate — blr_segment","text":"Event rate segements/levels qualitative variable.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event rate — blr_segment","text":"","code":"blr_segment(data, response, predictor) # Default S3 method blr_segment(data, response, predictor)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event rate — blr_segment","text":"data tibble data.frame. response Response variable; column data. predictor Predictor variable; column data.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event rate — blr_segment","text":"tibble.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event rate — blr_segment","text":"","code":"blr_segment(hsb2, honcomp, prog) #> Event By Attributes #> ------------------- #> pred 1s% #> ------------------- #> 1 0.04 #> 2 0.20 #> 3 0.03 #> ------------------- #>"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Response distribution — blr_segment_dist","title":"Response distribution — blr_segment_dist","text":"Distribution response variable segements/levels qualitative variable.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Response distribution — blr_segment_dist","text":"","code":"blr_segment_dist(data, response, predictor) # S3 method for class 'blr_segment_dist' plot( x, title = NA, xaxis_title = \"Levels\", yaxis_title = \"Sample Distribution\", sec_yaxis_title = \"1s Distribution\", bar_color = \"blue\", line_color = \"red\", print_plot = TRUE, ... )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Response distribution — blr_segment_dist","text":"data tibble data.frame. response Response variable; column data. predictor Predictor variable; column data. x object class blr_segment_dist. title Plot title. xaxis_title X axis title. yaxis_title Y axis title. sec_yaxis_title Secondary y axis title. bar_color Bar color. line_color Line color. print_plot logical; TRUE, prints plot else returns plot object. ... inputs.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Response distribution — blr_segment_dist","text":"tibble.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Response distribution — blr_segment_dist","text":"","code":"k <- blr_segment_dist(hsb2, honcomp, prog) k #> Event Segmentation #> ------------------------------------- #> pred n 1s n% 1s% #> ------------------------------------- #> 1 45 7 0.22 0.04 #> 2 105 40 0.52 0.20 #> 3 50 6 0.25 0.03 #> ------------------------------------- #> # plot plot(k)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_twoway.html","id":null,"dir":"Reference","previous_headings":"","what":"Two way event rate — blr_segment_twoway","title":"Two way event rate — blr_segment_twoway","text":"Event rate across two qualitative variables.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_twoway.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Two way event rate — blr_segment_twoway","text":"","code":"blr_segment_twoway(data, response, variable_1, variable_2) # Default S3 method blr_segment_twoway(data, response, variable_1, variable_2)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_twoway.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Two way event rate — blr_segment_twoway","text":"data tibble data.frame. response Response variable; column data. variable_1 Column data. variable_2 Column data.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_twoway.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Two way event rate — blr_segment_twoway","text":"tibble.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_segment_twoway.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Two way event rate — blr_segment_twoway","text":"","code":"blr_segment_twoway(hsb2, honcomp, prog, female) #> prog #> ----------------------- #> honcomp 0 1 #> ----------------------- #> 1 0.010 0.025 #> ----------------------- #> 2 0.075 0.125 #> ----------------------- #> 3 0.005 0.025 #> -----------------------"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise AIC backward elimination — blr_step_aic_backward","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"Build regression model set candidate predictor variables removing predictors based akaike information criterion, stepwise manner variable left remove .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"","code":"blr_step_aic_backward(model, ...) # Default S3 method blr_step_aic_backward(model, progress = FALSE, details = FALSE, ...) # S3 method for class 'blr_step_aic_backward' plot(x, text_size = 3, print_plot = TRUE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"model object class glm; model include candidate predictor variables. ... arguments. progress Logical; TRUE, display variable selection progress. details Logical; TRUE, print regression result step. x object class blr_step_aic_backward. text_size size text plot. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"blr_step_aic_backward returns object class \"blr_step_aic_backward\". object class \"blr_step_aic_backward\" list containing following components: model model least AIC; object class glm candidates candidate predictor variables steps total number steps predictors variables removed model aics akaike information criteria bics bayesian information criteria devs deviances","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"Venables, W. N. Ripley, B. D. (2002) Modern Applied Statistics S. Fourth edition. Springer.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_backward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise AIC backward elimination — blr_step_aic_backward","text":"","code":"if (FALSE) { # \\dontrun{ model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) # elimination summary blr_step_aic_backward(model) # print details of each step blr_step_aic_backward(model, details = TRUE) # plot plot(blr_step_aic_backward(model)) # final model k <- blr_step_aic_backward(model) k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise AIC selection — blr_step_aic_both","title":"Stepwise AIC selection — blr_step_aic_both","text":"Build regression model set candidate predictor variables entering removing predictors based akaike information criterion, stepwise manner variable left enter remove .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise AIC selection — blr_step_aic_both","text":"","code":"blr_step_aic_both(model, details = FALSE, ...) # S3 method for class 'blr_step_aic_both' plot(x, text_size = 3, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise AIC selection — blr_step_aic_both","text":"model object class lm. details Logical; TRUE, details variable selection printed screen. ... arguments. x object class blr_step_aic_both. text_size size text plot.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise AIC selection — blr_step_aic_both","text":"blr_step_aic_both returns object class \"blr_step_aic_both\". object class \"blr_step_aic_both\" list containing following components: model model least AIC; object class glm candidates candidate predictor variables predictors variables added/removed model method addition/deletion aics akaike information criteria bics bayesian information criteria devs deviances steps total number steps","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise AIC selection — blr_step_aic_both","text":"Venables, W. N. Ripley, B. D. (2002) Modern Applied Statistics S. Fourth edition. Springer.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_both.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise AIC selection — blr_step_aic_both","text":"","code":"if (FALSE) { # \\dontrun{ model <- glm(y ~ ., data = stepwise) # selection summary blr_step_aic_both(model) # print details at each step blr_step_aic_both(model, details = TRUE) # plot plot(blr_step_aic_both(model)) # final model k <- blr_step_aic_both(model) k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise AIC forward selection — blr_step_aic_forward","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"Build regression model set candidate predictor variables entering predictors based chi square statistic, stepwise manner variable left enter .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"","code":"blr_step_aic_forward(model, ...) # Default S3 method blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...) # S3 method for class 'blr_step_aic_forward' plot(x, text_size = 3, print_plot = TRUE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"model object class glm. ... arguments. progress Logical; TRUE, display variable selection progress. details Logical; TRUE, print regression result step. x object class blr_step_aic_forward. text_size size text plot. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"blr_step_aic_forward returns object class \"blr_step_aic_forward\". object class \"blr_step_aic_forward\" list containing following components: model model least AIC; object class glm candidates candidate predictor variables steps total number steps predictors variables entered model aics akaike information criteria bics bayesian information criteria devs deviances","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"Venables, W. N. Ripley, B. D. (2002) Modern Applied Statistics S. Fourth edition. Springer.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_aic_forward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise AIC forward selection — blr_step_aic_forward","text":"","code":"if (FALSE) { # \\dontrun{ model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) # selection summary blr_step_aic_forward(model) # print details of each step blr_step_aic_forward(model, details = TRUE) # plot plot(blr_step_aic_forward(model)) # final model k <- blr_step_aic_forward(model) k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise backward regression — blr_step_p_backward","title":"Stepwise backward regression — blr_step_p_backward","text":"Build regression model set candidate predictor variables removing predictors based p values, stepwise manner variable left remove .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise backward regression — blr_step_p_backward","text":"","code":"blr_step_p_backward(model, ...) # Default S3 method blr_step_p_backward(model, prem = 0.3, details = FALSE, ...) # S3 method for class 'blr_step_p_backward' plot(x, model = NA, print_plot = TRUE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise backward regression — blr_step_p_backward","text":"model object class lm; model include candidate predictor variables. ... inputs. prem p value; variables p prem removed model. details Logical; TRUE, print regression result step. x object class blr_step_p_backward. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise backward regression — blr_step_p_backward","text":"blr_step_p_backward returns object class \"blr_step_p_backward\". object class \"blr_step_p_backward\" list containing following components: model model least AIC; object class glm steps total number steps removed variables removed model aic akaike information criteria bic bayesian information criteria dev deviance indvar predictors","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise backward regression — blr_step_p_backward","text":"Chatterjee, Samprit Hadi, Ali. Regression Analysis Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_backward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise backward regression — blr_step_p_backward","text":"","code":"if (FALSE) { # \\dontrun{ # stepwise backward regression model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) blr_step_p_backward(model) # stepwise backward regression plot model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) k <- blr_step_p_backward(model) plot(k) # final model k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise regression — blr_step_p_both","title":"Stepwise regression — blr_step_p_both","text":"Build regression model set candidate predictor variables entering removing predictors based p values, stepwise manner variable left enter remove .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise regression — blr_step_p_both","text":"","code":"blr_step_p_both(model, ...) # Default S3 method blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...) # S3 method for class 'blr_step_p_both' plot(x, model = NA, print_plot = TRUE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise regression — blr_step_p_both","text":"model object class lm; model include candidate predictor variables. ... arguments. pent p value; variables p value less pent enter model. prem p value; variables p prem removed model. details Logical; TRUE, print regression result step. x object class blr_step_p_both. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise regression — blr_step_p_both","text":"blr_step_p_both returns object class \"blr_step_p_both\". object class \"blr_step_p_both\" list containing following components: model final model; object class glm orders candidate predictor variables according order added removed model method addition/deletion steps total number steps predictors variables retained model (addition) aic akaike information criteria bic bayesian information criteria dev deviance indvar predictors","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise regression — blr_step_p_both","text":"Chatterjee, Samprit Hadi, Ali. Regression Analysis Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_both.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise regression — blr_step_p_both","text":"","code":"if (FALSE) { # \\dontrun{ # stepwise regression model <- glm(y ~ ., data = stepwise) blr_step_p_both(model) # stepwise regression plot model <- glm(y ~ ., data = stepwise) k <- blr_step_p_both(model) plot(k) # final model k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":null,"dir":"Reference","previous_headings":"","what":"Stepwise forward regression — blr_step_p_forward","title":"Stepwise forward regression — blr_step_p_forward","text":"Build regression model set candidate predictor variables entering predictors based p values, stepwise manner variable left enter .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stepwise forward regression — blr_step_p_forward","text":"","code":"blr_step_p_forward(model, ...) # Default S3 method blr_step_p_forward(model, penter = 0.3, details = FALSE, ...) # S3 method for class 'blr_step_p_forward' plot(x, model = NA, print_plot = TRUE, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stepwise forward regression — blr_step_p_forward","text":"model object class lm; model include candidate predictor variables. ... arguments. penter p value; variables p value less penter enter model details Logical; TRUE, print regression result step. x object class blr_step_p_forward. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stepwise forward regression — blr_step_p_forward","text":"blr_step_p_forward returns object class \"blr_step_p_forward\". object class \"blr_step_p_forward\" list containing following components: model model least AIC; object class glm steps number steps predictors variables added model aic akaike information criteria bic bayesian information criteria dev deviance indvar predictors","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Stepwise forward regression — blr_step_p_forward","text":"Chatterjee, Samprit Hadi, Ali. Regression Analysis Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print. Kutner, MH, Nachtscheim CJ, Neter J Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_step_p_forward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stepwise forward regression — blr_step_p_forward","text":"","code":"if (FALSE) { # \\dontrun{ # stepwise forward regression model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_step_p_forward(model) # stepwise forward regression plot model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) k <- blr_step_p_forward(model) plot(k) # final model k$model } # }"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_hosmer_lemeshow.html","id":null,"dir":"Reference","previous_headings":"","what":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","title":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","text":"Hosmer lemeshow goodness fit test.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_hosmer_lemeshow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","text":"","code":"blr_test_hosmer_lemeshow(model, data = NULL)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_hosmer_lemeshow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","text":"model object class glm. data tibble data.frame.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_hosmer_lemeshow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","text":"Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). New York: John Wiley & Sons.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_hosmer_lemeshow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hosmer lemeshow test — blr_test_hosmer_lemeshow","text":"","code":"model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_test_hosmer_lemeshow(model) #> Partition for the Hosmer & Lemeshow Test #> -------------------------------------------------------------- #> def = 1 def = 0 #> Group Total Observed Expected Observed Expected #> -------------------------------------------------------------- #> 1 20 0 0.16 20 19.84 #> 2 20 0 0.53 20 19.47 #> 3 20 2 0.99 18 19.01 #> 4 20 1 1.64 19 18.36 #> 5 21 3 2.72 18 18.28 #> 6 19 3 4.05 16 14.95 #> 7 20 7 6.50 13 13.50 #> 8 20 10 8.90 10 11.10 #> 9 20 13 11.49 7 8.51 #> 10 20 14 16.02 6 3.98 #> -------------------------------------------------------------- #> #> Goodness of Fit Test #> ------------------------------ #> Chi-Square DF Pr > ChiSq #> ------------------------------ #> 4.4998 8 0.8095 #> ------------------------------"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood ratio test — blr_test_lr","title":"Likelihood ratio test — blr_test_lr","text":"Performs likelihood ratio test full reduced model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood ratio test — blr_test_lr","text":"","code":"blr_test_lr(full_model, reduced_model) # Default S3 method blr_test_lr(full_model, reduced_model)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood ratio test — blr_test_lr","text":"full_model object class glm; model predictors. reduced_model object class glm; nested model. Optional comparing full_model intercept model.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Likelihood ratio test — blr_test_lr","text":"Two tibbles model information test results.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_test_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood ratio test — blr_test_lr","text":"","code":"# compare full model with intercept only model # full model model_1 <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_test_lr(model_1) # compare full model with nested model # nested model model_2 <- glm(honcomp ~ female + read, data = hsb2, family = binomial(link = 'logit')) blr_test_lr(model_1, model_2)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":null,"dir":"Reference","previous_headings":"","what":"WoE & IV — blr_woe_iv","title":"WoE & IV — blr_woe_iv","text":"Weight evidence information value. Currently avialable categorical predictors .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"WoE & IV — blr_woe_iv","text":"","code":"blr_woe_iv(data, predictor, response, digits = 4, ...) # S3 method for class 'blr_woe_iv' plot( x, title = NA, xaxis_title = \"Levels\", yaxis_title = \"WoE\", bar_color = \"blue\", line_color = \"red\", print_plot = TRUE, ... )"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"WoE & IV — blr_woe_iv","text":"data tibble data.frame. predictor Predictor variable; column data. response Response variable; column data. digits Number decimal digits round . ... inputs. x object class blr_segment_dist. title Plot title. xaxis_title X axis title. yaxis_title Y axis title. bar_color Color bar. line_color Color horizontal line. print_plot logical; TRUE, prints plot else returns plot object.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"WoE & IV — blr_woe_iv","text":"tibble.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"WoE & IV — blr_woe_iv","text":"Siddiqi N (2006): Credit Risk Scorecards: developing implementing intelligent credit scoring. New Jersey, Wiley.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WoE & IV — blr_woe_iv","text":"","code":"# woe and iv k <- blr_woe_iv(hsb2, female, honcomp) k #> Weight of Evidence #> ------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 0 73 18 0.50 0.34 0.38 0.06 #> 1 74 35 0.50 0.66 -0.27 0.04 #> ------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> female 0.1023 #> ----------------------------- # plot woe plot(k)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi variable WOE & IV — blr_woe_iv_stats","title":"Multi variable WOE & IV — blr_woe_iv_stats","text":"Prints weight evidence information value multiple variables. Currently avialable categorical predictors .","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi variable WOE & IV — blr_woe_iv_stats","text":"","code":"blr_woe_iv_stats(data, response, ...)"},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi variable WOE & IV — blr_woe_iv_stats","text":"data data.frame tibble. response Response variable; column data. ... Predictor variables; column data.","code":""},{"path":[]},{"path":"https://blorr.rsquaredacademy.com/reference/blr_woe_iv_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi variable WOE & IV — blr_woe_iv_stats","text":"","code":"blr_woe_iv_stats(hsb2, honcomp, prog, race, female, schtyp) #> Variable: prog #> #> Weight of Evidence #> ------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 38 7 0.26 0.13 0.67 0.08 #> 2 65 40 0.44 0.75 -0.53 0.17 #> 3 44 6 0.30 0.11 0.97 0.18 #> ------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> prog 0.4329 #> ----------------------------- #> #> #> Variable: race #> #> Weight of Evidence #> ------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 22 2 0.15 0.04 1.38 0.15 #> 2 6 5 0.04 0.09 -0.84 0.04 #> 3 18 2 0.12 0.04 1.18 0.10 #> 4 101 44 0.69 0.83 -0.19 0.03 #> ------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> race 0.326 #> ----------------------------- #> #> #> Variable: female #> #> Weight of Evidence #> ------------------------------------------------------------------------- #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 0 73 18 0.50 0.34 0.38 0.06 #> 1 74 35 0.50 0.66 -0.27 0.04 #> ------------------------------------------------------------------------- #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> female 0.1023 #> ----------------------------- #> #> #> Variable: schtyp #> #> Weight of Evidence #> ------------------------------------------------------------------------ #> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------ #> 1 123 45 0.84 0.85 -0.01 0.00 #> 2 24 8 0.16 0.15 0.08 0.00 #> ------------------------------------------------------------------------ #> #> Information Value #> ----------------------------- #> Variable Information Value #> ----------------------------- #> schtyp 0.0012 #> ----------------------------- #> #>"},{"path":"https://blorr.rsquaredacademy.com/reference/hsb2.html","id":null,"dir":"Reference","previous_headings":"","what":"High School and Beyond Data Set — hsb2","title":"High School and Beyond Data Set — hsb2","text":"dataset containing demographic information standardized test scores high school students.","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/hsb2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"High School and Beyond Data Set — hsb2","text":"","code":"hsb2"},{"path":"https://blorr.rsquaredacademy.com/reference/hsb2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"High School and Beyond Data Set — hsb2","text":"data frame 200 rows 11 variables: id id student female gender student race ethnic background student ses socio-economic status student schtyp school type prog program type read scores test reading write scores test writing math scores test math science scores test science socst scores test social studies honcomp 1 write > 60, else 0","code":""},{"path":"https://blorr.rsquaredacademy.com/reference/hsb2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"High School and Beyond Data Set — 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