diff --git a/dev/news/index.html b/dev/news/index.html
index ead8094..4191419 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -41,9 +41,14 @@
 
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.0.9000" id="mwana-v0109000-development-version">mwana v0.1.0.9000 (development version)<a class="anchor" aria-label="anchor" href="#mwana-v0109000-development-version"></a></h2>
+<p><br></p>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.1.0" id="mwana-v010">mwana v0.1.0<a class="anchor" aria-label="anchor" href="#mwana-v010"></a></h2>
 <ul><li>Initial pre-release version for alpha-testing.</li>
 </ul></div>
-  </main></div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index f989a60..b85e8e3 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   ipc_amn_check: ipc_amn_check.html
   plausibility: plausibility.html
   prevalence: prevalence.html
-last_built: 2024-11-04T18:51Z
+last_built: 2024-11-04T21:44Z
 urls:
   reference: https://nutriverse.io/mwana/reference
   article: https://nutriverse.io/mwana/articles
diff --git a/dev/search.json b/dev/search.json
index 414f4f0..de163e1 100644
--- a/dev/search.json
+++ b/dev/search.json
@@ -1 +1 @@
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General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. 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Mere interaction user computer network, transfer copy, conveying. interactive user interface displays “Appropriate Legal Notices” extent includes convenient prominently visible feature (1) displays appropriate copyright notice, (2) tells user warranty work (except extent warranties provided), licensees may convey work License, view copy License. interface presents list user commands options, menu, prominent item list meets criterion.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_1-source-code","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"1. 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License explicitly affirms unlimited permission run unmodified Program. output running covered work covered License output, given content, constitutes covered work. License acknowledges rights fair use equivalent, provided copyright law. may make, run propagate covered works convey, without conditions long license otherwise remains force. may convey covered works others sole purpose make modifications exclusively , provide facilities running works, provided comply terms License conveying material control copyright. thus making running covered works must exclusively behalf, direction control, terms prohibit making copies copyrighted material outside relationship . Conveying circumstances permitted solely conditions stated . Sublicensing allowed; section 10 makes unnecessary.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_3-protecting-users-legal-rights-from-anti-circumvention-law","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"3. 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License gives permission license work way, invalidate permission separately received . d) work interactive user interfaces, must display Appropriate Legal Notices; however, Program interactive interfaces display Appropriate Legal Notices, work need make . compilation covered work separate independent works, nature extensions covered work, combined form larger program, volume storage distribution medium, called “aggregate” compilation resulting copyright used limit access legal rights compilation’s users beyond individual works permit. Inclusion covered work aggregate cause License apply parts aggregate.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_6-conveying-non-source-forms","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"6. 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Regardless server hosts Corresponding Source, remain obligated ensure available long needed satisfy requirements. e) Convey object code using peer--peer transmission, provided inform peers object code Corresponding Source work offered general public charge subsection 6d. separable portion object code, whose source code excluded Corresponding Source System Library, need included conveying object code work. “User Product” either (1) “consumer product”, means tangible personal property normally used personal, family, household purposes, (2) anything designed sold incorporation dwelling. determining whether product consumer product, doubtful cases shall resolved favor coverage. particular product received particular user, “normally used” refers typical common use class product, regardless status particular user way particular user actually uses, expects expected use, product. product consumer product regardless whether product substantial commercial, industrial non-consumer uses, unless uses represent significant mode use product. “Installation Information” User Product means methods, procedures, authorization keys, information required install execute modified versions covered work User Product modified version Corresponding Source. information must suffice ensure continued functioning modified object code case prevented interfered solely modification made. convey object code work section , , specifically use , User Product, conveying occurs part transaction right possession use User Product transferred recipient perpetuity fixed term (regardless transaction characterized), Corresponding Source conveyed section must accompanied Installation Information. requirement apply neither third party retains ability install modified object code User Product (example, work installed ROM). requirement provide Installation Information include requirement continue provide support service, warranty, updates work modified installed recipient, User Product modified installed. 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Additional permissions applicable entire Program shall treated though included License, extent valid applicable law. additional permissions apply part Program, part may used separately permissions, entire Program remains governed License without regard additional permissions. convey copy covered work, may option remove additional permissions copy, part . (Additional permissions may written require removal certain cases modify work.) may place additional permissions material, added covered work, can give appropriate copyright permission. Notwithstanding provision License, material add covered work, may (authorized copyright holders material) supplement terms License terms: ) Disclaiming warranty limiting liability differently terms sections 15 16 License; b) Requiring preservation specified reasonable legal notices author attributions material Appropriate Legal Notices displayed works containing ; c) Prohibiting misrepresentation origin material, requiring modified versions material marked reasonable ways different original version; d) Limiting use publicity purposes names licensors authors material; e) Declining grant rights trademark law use trade names, trademarks, service marks; f) Requiring indemnification licensors authors material anyone conveys material (modified versions ) contractual assumptions liability recipient, liability contractual assumptions directly impose licensors authors. non-permissive additional terms considered “restrictions” within meaning section 10. Program received , part , contains notice stating governed License along term restriction, may remove term. license document contains restriction permits relicensing conveying License, may add covered work material governed terms license document, provided restriction survive relicensing conveying. add terms covered work accord section, must place, relevant source files, statement additional terms apply files, notice indicating find applicable terms. Additional terms, permissive non-permissive, may stated form separately written license, stated exceptions; requirements apply either way.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_8-termination","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"8. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Running plausibility checks","text":"Plausibility check tool evaluates overall quality acceptability anthropometric data ensure suitability informing decision-making process. mwana provides set handy functions facilitate evaluation. functions allow users assess acceptability weight--height z-score (WFHZ) mid upper-arm circumference (MUAC) data. evaluation latter can done basis MUAC--age z-score (MFAZ) raw MUAC values. vignette, learn use functions consider using MFAZ plausibility check one based raw MUAC values. demonstration, use mwana built-sample dataset named anthro.01. dataset contains district level SMART surveys anonymized locations. ?anthro.01 R console read dataset. begin demonstration plausibility check familiar proceed ones less familiar . check plausibility WFHZ data calling mw_plausibility_check_wfhz() function. , need ensure data right “shape format” accepted understood function. Don’t worry, soon learn get . first, let’s take moment walk key features function. mw_plausibility_check_wfhz() replica plausibility check ENA SMART software SMART Methodology. hood, runs statistical tests already know SMART, applies rating scoring criteria. Beware though small differences mind: mw_plausibility_check_wfhz() include MUAC test suite. simply due fact now can run comprehensive test suite MUAC. mw_plausibility_check_wfhz() allows user run checks multiple area dataset , without repeat workflow number areas data holds. ! Now can begin delving “”. always good practice start inspecting dataset. Let’s check first 6 rows dataset: can see dataset eleven variables, way respective values presented. useful inform data wrangling workflow. mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see : can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 . assess plausibility MUAC data MFAZ age variable available dataset. Note plausibility check MFAZ data built based insights gotten Bilukha, O., & Kianian, B. (2023) research presented 2023 High-Level Technical Assessment Workshop held Nairobi, Kenya. Results research suggested feasibility applying similar plausibility check WFHZ MFAZ, difference amount flags considered: maximum 2%. can run MFAZ plausibility check calling mw_plausibility_check_mfaz(). WFHZ, first need ensure data right shape format accepted understood function. workflow starts processing age; , approach way Section 1.1.1.1. Age ratio test MFAZ know, age ratio test WFHZ done children aged 6 29 months old aged 30 59 months old. different MFAZ. test done children aged 6 23 months aged 24 59 months old. SMART MUAC Tool. test results also used prevalence analysis implement SMART MUAC tool . demonstrated vignette prevalence. job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments. achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨. assess plausibility raw MUAC data ’s raw values variable age available dataset. job assigned mw_plausibility_check_muac(). workflow check shortest one. can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns: just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"head(anthro.01) # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0 x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, … anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-wfhz-data","dir":"Articles","previous_headings":"","what":"Plausibility check of WFHZ data","title":"Running plausibility checks","text":"check plausibility WFHZ data calling mw_plausibility_check_wfhz() function. , need ensure data right “shape format” accepted understood function. Don’t worry, soon learn get . first, let’s take moment walk key features function. mw_plausibility_check_wfhz() replica plausibility check ENA SMART software SMART Methodology. hood, runs statistical tests already know SMART, applies rating scoring criteria. Beware though small differences mind: mw_plausibility_check_wfhz() include MUAC test suite. simply due fact now can run comprehensive test suite MUAC. mw_plausibility_check_wfhz() allows user run checks multiple area dataset , without repeat workflow number areas data holds. ! Now can begin delving “”. always good practice start inspecting dataset. Let’s check first 6 rows dataset: can see dataset eleven variables, way respective values presented. useful inform data wrangling workflow. mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see : can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 .","code":"head(anthro.01) # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0 x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, …"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"data-wrangling","dir":"Articles","previous_headings":"Introduction","what":"Data wrangling","title":"Running plausibility checks","text":"mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see :","code":"age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"sec-age","dir":"Articles","previous_headings":"Introduction","what":"Age","title":"Running plausibility checks","text":"use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return:","code":"age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"wrangling-all-other-remaining-variables","dir":"Articles","previous_headings":"Introduction","what":"Wrangling all other remaining variables","title":"Running plausibility checks","text":", call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see :","code":"wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check-of-wfhz-data","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check of WFHZ data","title":"Running plausibility checks","text":"can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 .","code":"x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, …"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-mfaz-data","dir":"Articles","previous_headings":"","what":"Plausibility check of MFAZ data","title":"Running plausibility checks","text":"assess plausibility MUAC data MFAZ age variable available dataset. Note plausibility check MFAZ data built based insights gotten Bilukha, O., & Kianian, B. (2023) research presented 2023 High-Level Technical Assessment Workshop held Nairobi, Kenya. Results research suggested feasibility applying similar plausibility check WFHZ MFAZ, difference amount flags considered: maximum 2%. can run MFAZ plausibility check calling mw_plausibility_check_mfaz(). WFHZ, first need ensure data right shape format accepted understood function. workflow starts processing age; , approach way Section 1.1.1.1. Age ratio test MFAZ know, age ratio test WFHZ done children aged 6 29 months old aged 30 59 months old. different MFAZ. test done children aged 6 23 months aged 24 59 months old. SMART MUAC Tool. test results also used prevalence analysis implement SMART MUAC tool . demonstrated vignette prevalence. job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments. achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨.","code":"anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"sec-wrangle_mfaz","dir":"Articles","previous_headings":"Introduction","what":"Wrangling MFAZ data","title":"Running plausibility checks","text":"job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments.","code":"anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check-of-mfaz-data","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check of MFAZ data","title":"Running plausibility checks","text":"achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨.","code":"## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-raw-muac-data","dir":"Articles","previous_headings":"","what":"Plausibility check of raw MUAC data","title":"Running plausibility checks","text":"assess plausibility raw MUAC data ’s raw values variable age available dataset. job assigned mw_plausibility_check_muac(). workflow check shortest one. can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns: just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"data-wrangling-1","dir":"Articles","previous_headings":"Introduction","what":"Data wrangling","title":"Running plausibility checks","text":"can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check","title":"Running plausibility checks","text":"just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Estimating the prevalence of wasting","text":"vignette demonstrates use mwana package’s functions estimate prevalence wasting. package allow users estimate prevalence based : weight--height z-score (WFHZ) /edema; raw MUAC values /edema; combined prevalence MUAC--age z-score (MFAZ) /edema. prevalence functions mwana carefully conceived designed simplify workflow nutrition data analyst, especially dealing datasets containing imperfections require additional layers analysis. Let’s try clarify two scenarios believe remind complexity involved: analysing multi-area dataset, users likely need estimate prevalence area individually. Afterward, must extract results collate summary table. working MUAC data, age ratio test rated problematic, additional tool required weight prevalence correct age bias. unfortunate cases multiple areas face issue, workflow must repeated several times, making process cumbersome highly error-prone 😬. mwana longer worry 🥳 functions designed deal . demonstrate use, use different datasets containing imperfections alluded : anthro.02 : survey data survey weights. Read data ?anthro.02. anthro.03 : district-level SMART surveys two districts whose WFHZ standard deviation rated problematic rest within range. ?anthro.03 details. anthro.04 : community-based sentinel site data. data different characteristics require different analysis approaches. Now can begin delving function. estimate prevalence wasting based WFHZ use compute_wfhz_prevalence() function. dataset supply must wrangled mw_wrangle_wfhz(). usual, start inspecting dataset: can see dataset contains required variables WFHZ prevalence analysis, including weighted analysis. dataset already wrangled, need call WFHZ wrangler case. begin demonstration unweigthed analysis - typical SMART surveys - proceed weighted analysis. achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics. get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done. prevalence wasting based MFAZ can estimated using compute_mfaz_prevalence() function. function works implemented way demonstrated Section 1.1, exception data wrangling based MUAC. demonstrated plausibility checks. way, avoid redundancy, demonstrate workflow. job assigned compute_muac_prevalence(). call function, starting prevalence estimation, first evaluates acceptability MFAZ standard deviation age ratio test. Yes, read well, MFAZ’s standard deviation, raw values MUAC. Important Although acceptability evaluated basis MFAZ, actual prevalence estimated basis raw MUAC values. MFAZ also used detect outliers flag excluded prevalence analysis. MFAZ standard deviation age ratio test results used control prevalence analysis flow way: MFAZ standard deviation age ratio test problematic, standard complex sample-based prevalence estimated. MFAZ standard deviation problematic age ratio test problematic, CDC/SMART MUAC tool age-weighting approach applied. MFAZ standard deviation problematic, even age ratio problematic, prevalence analysis estimated, instead NA thrown. working multiple-area dataset, conditionals still applied according area’s situation. work multi-area dataset Fundamentally, function performs standard deviation age ratio tests, evaluates acceptability, returns summarized table area. iterates summary table row row checking conditionals. Based conditionals row (area), function accesses original dataset, computes prevalence accordingly, returns results. demonstrate use anthro.04 dataset. usual, let’s first inspect : see data already wrangled, go straight prevalence estimation. Important ENA Software, make sure run plausibility check call prevalence function. good know acceptability data. anthro.04 see province issues, hence expecting see demonstrations based conditionals stated . return: see Province 1, columns filled numbers; Province 2, columns filled numbers, columns filled NAs: age-weighting approach applied. Lastly, Province 3 bunch NA filled everywhere - know 😉 . go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters. estimation combined prevalence wasting task attributed compute_combined_prevalence() function. case-definition based WFHZ, raw MUAC values edema. workflow standpoint, combines workflow demonstrated Section 1.1 Section 1.3. demonstrate ’s implementation use anthro.01 dataset. Let’s inspect data: Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"tail(anthro.02) # A tibble: 6 × 14   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> 1 Nampula  Urban      285     1  59.5   13.8   90.7 n       149     487.  0.689 2 Nampula  Rural      234     1  59.5   17.2  105.  n       193    1045.  0.178 3 Nampula  Rural      263     1  59.6   18.4  100   n       156     952.  2.13 4 Nampula  Rural      257     1  59.7   15.9  100.  n       149     987.  0.353 5 Nampula  Rural      239     1  59.8   12.5   91.5 n       135     663. -0.722 6 Nampula  Rural      263     1  60.0   14.3   93.8 n       142     952.  0.463 # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 8   province   cluster   sex   age  muac edema   mfaz flag_mfaz   <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> 1 Province 3     743     2    21   130 n     -1.50          0 2 Province 3     743     2     9   126 n     -1.33          0 3 Province 3     743     2    12   128 n     -1.27          0 4 Province 3     743     2    34   145 n     -0.839         0 5 Province 3     743     2    11   130 n     -1.04          0 6 Province 3     743     2    33   140 n     -1.23          0 anthro.04 |>   compute_muac_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province   ) # A tibble: 3 × 17   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA 3 Province 3    NA NA       NA         NA             NA    NA NA       NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> ## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> ## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"sec-prevalence-wfhz","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on WFHZ","title":"Estimating the prevalence of wasting","text":"estimate prevalence wasting based WFHZ use compute_wfhz_prevalence() function. dataset supply must wrangled mw_wrangle_wfhz(). usual, start inspecting dataset: can see dataset contains required variables WFHZ prevalence analysis, including weighted analysis. dataset already wrangled, need call WFHZ wrangler case. begin demonstration unweigthed analysis - typical SMART surveys - proceed weighted analysis. achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics. get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done.","code":"tail(anthro.02) # A tibble: 6 × 14   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> 1 Nampula  Urban      285     1  59.5   13.8   90.7 n       149     487.  0.689 2 Nampula  Rural      234     1  59.5   17.2  105.  n       193    1045.  0.178 3 Nampula  Rural      263     1  59.6   18.4  100   n       156     952.  2.13 4 Nampula  Rural      257     1  59.7   15.9  100.  n       149     987.  0.353 5 Nampula  Rural      239     1  59.8   12.5   91.5 n       135     663. -0.722 6 Nampula  Rural      263     1  60.0   14.3   93.8 n       142     952.  0.463 # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-unweighted-prevalence","dir":"Articles","previous_headings":"","what":"Estimation of unweighted prevalence","title":"Estimating the prevalence of wasting","text":"achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics.","code":"anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-weighted-prevalence","dir":"Articles","previous_headings":"","what":"Estimation of weighted prevalence","title":"Estimating the prevalence of wasting","text":"get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done.","code":"anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-the-prevalence-of-wasting-based-on-mfaz","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on MFAZ","title":"Estimating the prevalence of wasting","text":"prevalence wasting based MFAZ can estimated using compute_mfaz_prevalence() function. function works implemented way demonstrated Section 1.1, exception data wrangling based MUAC. demonstrated plausibility checks. way, avoid redundancy, demonstrate workflow.","code":""},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"sec-prevalence-muac","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on the raw MUAC values","title":"Estimating the prevalence of wasting","text":"job assigned compute_muac_prevalence(). call function, starting prevalence estimation, first evaluates acceptability MFAZ standard deviation age ratio test. Yes, read well, MFAZ’s standard deviation, raw values MUAC. Important Although acceptability evaluated basis MFAZ, actual prevalence estimated basis raw MUAC values. MFAZ also used detect outliers flag excluded prevalence analysis. MFAZ standard deviation age ratio test results used control prevalence analysis flow way: MFAZ standard deviation age ratio test problematic, standard complex sample-based prevalence estimated. MFAZ standard deviation problematic age ratio test problematic, CDC/SMART MUAC tool age-weighting approach applied. MFAZ standard deviation problematic, even age ratio problematic, prevalence analysis estimated, instead NA thrown. working multiple-area dataset, conditionals still applied according area’s situation. work multi-area dataset Fundamentally, function performs standard deviation age ratio tests, evaluates acceptability, returns summarized table area. iterates summary table row row checking conditionals. Based conditionals row (area), function accesses original dataset, computes prevalence accordingly, returns results. demonstrate use anthro.04 dataset. usual, let’s first inspect : see data already wrangled, go straight prevalence estimation. Important ENA Software, make sure run plausibility check call prevalence function. good know acceptability data. anthro.04 see province issues, hence expecting see demonstrations based conditionals stated . return: see Province 1, columns filled numbers; Province 2, columns filled numbers, columns filled NAs: age-weighting approach applied. Lastly, Province 3 bunch NA filled everywhere - know 😉 . go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters.","code":"# A tibble: 6 × 8   province   cluster   sex   age  muac edema   mfaz flag_mfaz   <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> 1 Province 3     743     2    21   130 n     -1.50          0 2 Province 3     743     2     9   126 n     -1.33          0 3 Province 3     743     2    12   128 n     -1.27          0 4 Province 3     743     2    34   145 n     -0.839         0 5 Province 3     743     2    11   130 n     -1.04          0 6 Province 3     743     2    33   140 n     -1.23          0 anthro.04 |>   compute_muac_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province   ) # A tibble: 3 × 17   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA 3 Province 3    NA NA       NA         NA             NA    NA NA       NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> ## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-weighted-prevalence-1","dir":"Articles","previous_headings":"","what":"Estimation of weighted prevalence","title":"Estimating the prevalence of wasting","text":"go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters.","code":"## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-the-combined-prevalence-of-wasting","dir":"Articles","previous_headings":"","what":"Estimation of the combined prevalence of wasting","title":"Estimating the prevalence of wasting","text":"estimation combined prevalence wasting task attributed compute_combined_prevalence() function. case-definition based WFHZ, raw MUAC values edema. workflow standpoint, combines workflow demonstrated Section 1.1 Section 1.3. demonstrate ’s implementation use anthro.01 dataset. Let’s inspect data: Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"# A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> ## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"data-wrangling","dir":"Articles","previous_headings":"","what":"Data wrangling","title":"Estimating the prevalence of wasting","text":"Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomás Zaba. Author, maintainer, copyright holder. Ernest Guevarra. Author, copyright holder.","code":""},{"path":"https://nutriverse.io/mwana/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Tomás Zaba, Ernest Guevarra (2024). mwana: Utilities Analysing Children's Nutritional Status. R package version 0.0.0.9000, https://github.com/nutriverse/mwana.","code":"@Manual{,   title = {mwana: Utilities for Analysing Children's Nutritional Status},   author = {{Tomás Zaba} and {Ernest Guevarra}},   year = {2024},   note = {R package version 0.0.0.9000},   url = {https://github.com/nutriverse/mwana}, }"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"mwana-utilities-for-analysing-childrens-nutritional-status-","dir":"","previous_headings":"","what":"Utilities for Analysing Children's Nutritional Status","title":"Utilities for Analysing Children's Nutritional Status","text":"Child anthropometric assessments, implemented routinely countries worldwide, cornerstones child nutrition food security surveillance around world. Ensuring quality child anthropometric data, accuracy child undernutrition prevalence estimates, timeliness reporting therefore critical establishing accurate, robust, --date child undernutrition status globally. mwana, term child Elómwè, local language spoken central-northern regions Mozambique, also word similar meaning across Bantu languages (Swahili) spoken many parts Africa, package streamlines child anthropometry data quality checks undernutrition prevalence estimation children 6-59 months old comprehensive implementation SMART Methodology guidelines R.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana borne author’s experience work multiple child anthropometric datasets conduct data quality appraisal prevalence estimation part data quality assurance team Integrated Phase Classification (IPC). current standard child anthropometric data appraisal workflow extremely cumbersome requiring significant time effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment ENA software) step process single dataset. process repeated every dataset needing processed often needing implemented relatively short period time. manual repetitive process, nature, extremely error-prone. mwana, primarily R-based implementation ENA SMART software, simplifies cumbersome workflow programmable process particularly handling large multiple datasets. [!NOTE] mwana made possible thanks state---art work nutrition survey guidance led SMART initiative. hood, mwana bundles SMART guidance use National Information Platforms Nutrition Anthropometric Data Toolkit (nipnTK) functionalities R build handy function around plausibility checks. Click learn nipnTK package.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"what-does-mwana-do","dir":"","previous_headings":"","what":"What does mwana do?","title":"Utilities for Analysing Children's Nutritional Status","text":"automates plausibility checks prevalence analyses respective summaries outputs.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"plausibility-checks","dir":"","previous_headings":"What does mwana do?","what":"Plausibility checks.","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana performs plausibility checks weight--height z-score (WFHZ)-based data mimicking SMART plausibility checkers ENA SMART software, scoring classification criterion. Read guide . performs, well, plausibility checks MUAC data. , mwana integrates recent advances using MUAC--age z-score (MFAZ) assessing plausibility acceptability MUAC data. way, variable age available: mwana performs plausibility checks similar WFHZ, differences scoring criteria flagged data. Otherwise, variables age missing, similar test suit used current version ENA performed. Read guide .","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"prevalence-analysis","dir":"","previous_headings":"What does mwana do?","what":"Prevalence analysis","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana prevalence calculators built take decisions appropriate analysis procedure follow based quality data, per SMART rules. returns output table appropriate results based data quality test results. Fundamentally, calculators loop survey areas dataset whilst performing quality appraisal taking decisions appropriate prevalence analysis procedure follow basis result. mwana computes prevalence : Wasting basis WFHZ /edema; read guide Wasting basis raw MUAC values /edema: , variable age available, mwana applies MFAZ flags, otherwise applies flagging criteria around raw MUAC values, exclude outliers computing prevalence, actual prevalence done raw values; read guide . Wasting basis MFAZ /edema: outliers excluded using MFAZ flags; read guide . Combined prevalence wasting: concept combined flags used streamline flags removed WFHZ MUAC; read guide . mwana provides weighted prevalence analysis, needed. controlled user. possible calculators, including MUAC, combined, currently available ENA SMART. context IPC Acute Malnutrition (IPC AMN) analysis workflow, mwana provides handy function checking minimum sample size requirements given area met basis methodology used collect data: survey, screening sentinel sites. Read guide . [!TIP] undertaking research want wrangle data including statistical models, etc, mwana great helper. [!WARNING] Please note mwana still highly experimental undergoing lot development. Hence, functionalities described high likelihood changing interface approach aim stable working version.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana yet CRAN can installed nutriverse R Universe follows: load memory ","code":"install.packages(   \"mwana\",   repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org') ) library(mwana)"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Utilities for Analysing Children's Nutritional Status","text":"enticed use mwana package found useful, please cite using suggested citation provided call citation function follows:","code":"citation(\"mwana\") #> To cite mwana: in publications use: #>  #>   Tomás Zaba, Ernest Guevarra (2024). _mwana: Utilities for Analysing #>   Children's Nutritional Status_. R package version 0.0.0.9000, #>   <https://github.com/nutriverse/mwana>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {mwana: Utilities for Analysing Children's Nutritional Status}, #>     author = {{Tomás Zaba} and {Ernest Guevarra}}, #>     year = {2024}, #>     note = {R package version 0.0.0.9000}, #>     url = {https://github.com/nutriverse/mwana}, #>   }"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"community-guidelines","dir":"","previous_headings":"","what":"Community guidelines","title":"Utilities for Analysing Children's Nutritional Status","text":"Feedback, bug reports feature requests welcome; file issues seek support . like contribute package, please see contributing guidelines. project releases Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of district level SMART surveys with location anonymised — anthro.01","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"anthro.01 two-stage cluster-based survey probability selection clusters proportional size population. survey employed SMART methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"","code":"anthro.01"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"tibble 1,191 rows 11 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"","code":"anthro.01 #> # A tibble: 1,191 × 11 #>    area      dos        cluster  team sex   dob      age weight height edema #>    <chr>     <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> #>  1 District… 2023-12-04       1     3 m     NA        59   15.6  109.  n     #>  2 District… 2023-12-04       1     3 m     NA         8    7.5   68.6 n     #>  3 District… 2023-12-04       1     3 m     NA        19    9.7   79.5 n     #>  4 District… 2023-12-04       1     3 f     NA        49   14.3  100.  n     #>  5 District… 2023-12-04       1     3 f     NA        32   12.4   92.1 n     #>  6 District… 2023-12-04       1     3 f     NA        17    9.3   77.8 n     #>  7 District… 2023-12-04       1     3 f     NA        20   10.1   80.4 n     #>  8 District… 2023-12-04       1     3 f     NA        27   11.7   87.1 n     #>  9 District… 2023-12-04       1     3 m     NA        46   13.6   98   n     #> 10 District… 2023-12-04       1     3 m     NA        58   17.2  109.  n     #> # ℹ 1,181 more rows #> # ℹ 1 more variable: muac <int>"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample of an already wrangled survey data — anthro.02","title":"A sample of an already wrangled survey data — anthro.02","text":"household budget survey data conducted Mozambique 2019/2020, known IOF (Inquérito ao Orçamento Familiar Portuguese). IOF two-stage cluster-based survey, representative province level (admin 2), probability selection clusters proportional size population. data collection spans period 12 months.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample of an already wrangled survey data — anthro.02","text":"","code":"anthro.02"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample of an already wrangled survey data — anthro.02","text":"tibble 2,267 rows 14 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample of an already wrangled survey data — anthro.02","text":"Mozambique National Institute Statistics. data publicly available https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-data_access. Data wrangled using package's wranglers. Details survey design can gotten : https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-sampling","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample of an already wrangled survey data — anthro.02","text":"","code":"anthro.02 #> # A tibble: 2,267 × 14 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #>  3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n       123     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n       126     165. -0.349 #>  9 Zambezia Urban      502     1  6.41    7.5   66   n       142    1083. -0.006 #> 10 Zambezia Urban      500     1  6.41    6.8   64.1 n       135     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"anthro.03 contains survey data four districts. district dataset presents distinct data quality scenarios requires tailored prevalence analysis approach: two districts show problematic WFHZ standard deviation whilst remaining within range. sample data useful demonstrate use prevalence functions multi-area survey data can variations rating acceptability standard deviation, hence require different analyses approaches area ensure accurate estimation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"","code":"anthro.03"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"tibble 943 x 9.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"","code":"anthro.03 #> # A tibble: 943 × 9 #>    district cluster  team sex     age weight height edema  muac #>    <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> #>  1 Metuge         2     2 m      9.99   10.1   69.3 n       172 #>  2 Metuge         2     2 f     43.6    10.9   91.5 n       130 #>  3 Metuge         2     2 f     32.8    11.4   91.4 n       153 #>  4 Metuge         2     2 f      7.62    8.3   69.5 n       133 #>  5 Metuge         2     2 m     28.4    10.7   82.3 n       143 #>  6 Metuge         2     2 f     12.3     6.6   69.4 n       121 #>  7 Metuge         2     2 f     32.0    11.1   85.2 n       148 #>  8 Metuge         2     2 m     34.9    12.6   86.5 n       156 #>  9 Metuge         3     3 m      9.07    8.3   71.4 n       145 #> 10 Metuge         3     3 m     45.5    11.5   85.7 n       145 #> # ℹ 933 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"Data generated community-based sentinel site conducted across three provinces. province's dataset presents distinct data quality scenarios, requiring tailored prevalence analysis: \"Province 1\" MFAZ's standard deviation age ratio test rating acceptability falling within range; \"Province 2\" age ratio rated problematic acceptable standard deviation MFAZ; \"Province 3\" tests rated problematic. sample data useful demonstrate use prevalence functions multi-area survey data variations rating acceptability standard deviation exist, hence require different analyses approaches area ensure accurate estimation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"","code":"anthro.04"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"tibble 3,002 x 8.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"","code":"anthro.04 #> # A tibble: 3,002 × 8 #>    province   cluster   sex   age  muac edema   mfaz flag_mfaz #>    <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> #>  1 Province 1     298     2    24   136 n     -1.12          0 #>  2 Province 1     298     2    30   116 n     -3.44          0 #>  3 Province 1     298     2     7   140 n      0.084         0 #>  4 Province 1     298     2    18   144 n     -0.068         0 #>  5 Province 1     298     2    10   125 n     -1.48          0 #>  6 Province 1     298     2    11   125 n     -1.52          0 #>  7 Province 1     298     2    30   136 n     -1.46          0 #>  8 Province 1     298     2    24   133 n     -1.40          0 #>  9 Province 1     298     2    10   122 n     -1.78          0 #> 10 Province 1     298     2    24   142 n     -0.579         0 #> # ℹ 2,992 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"Calculate weighted prevalence estimate MUAC adding proportion children 2 years twice proportion children 2 dividing 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"","code":"apply_cdc_age_weighting(muac, age, .edema = NULL, status = c(\"sam\", \"mam\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"muac vector class integer MUAC values (mm). age vector class double child's age months. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. status choice form wasting defined.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"vector class numeric length size 1.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"function informed output mw_stattest_ageratio().","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":null,"dir":"Reference","previous_headings":"","what":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"Define given observation dataset wasted , basis WFHZ, MFAZ, MUAC combined criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"","code":"define_wasting_cases_muac(muac, edema = NULL, cases = c(\"gam\", \"sam\", \"mam\"))  define_wasting_cases_whz(zscore, edema = NULL, cases = c(\"gam\", \"sam\", \"mam\"))  define_wasting_cases_combined(   zscore,   muac,   edema = NULL,   cases = c(\"cgam\", \"csam\", \"cmam\") )  define_wasting(   df,   zscore = NULL,   muac = NULL,   edema = NULL,   base = c(\"wfhz\", \"muac\", \"combined\") )"},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"muac vector class integer MUAC values millimeters. edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. cases choice form wasting defined. zscore vector class double WFHZ values (3 decimal places). df dataset object class data.frame use. base choice criterion case-definition based.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"vector class numeric dummy values: 1 case 0 case.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"Use define_wasting() add case-definitions data frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"","code":"## Weight-for-height based case-definition ---- x <- anthro.02 |> define_wasting( zscore = wfhz, edema = edema, base = \"wfhz\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, gam <dbl>, #> #   sam <dbl>, mam <dbl>  ## MUAC-based case-definition ---- x <- anthro.02 |> define_wasting( muac = muac, edema = edema, base = \"muac\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, gam <dbl>, #> #   sam <dbl>, mam <dbl>  ## Combined case-definition ---- x <- anthro.02 |> define_wasting( zscore = wfhz, muac = muac, edema = edema, base = \"combined\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, cgam <dbl>, #> #   csam <dbl>, cmam <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":null,"dir":"Reference","previous_headings":"","what":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"Classify wasting severe moderate wasting used SMART MUAC tool weighting approach","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"","code":"classify_wasting_for_cdc_approach(muac, .edema = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"muac vector class integer MUAC values millimeters. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"vector class character length muac .edema indicating child severe moderately wasted wasted.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"prevalence calculated accordance complex sample design properties inherent surveys. includes weighting survey data applicable. either acceptability standard deviation WFHZ age ratio test problematic, prevalence calculated.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"","code":"compute_pps_based_combined_prevalence(   df,   .wt = NULL,   .edema = NULL,   .summary_by )  compute_combined_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"df already wrangled dataset class data.frame use. wranglers (WFHZ MUAC) need used sequentially, regardless order. Note MUAC values converted millimeters using MUAC wrangler. .wt vector class double final survey weights. Default NULL assuming self-weighted survey, ENA SMART software; otherwise weighted analysis computed. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"summarised table class data.frame descriptive statistics combined wasting.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"concept \"combined flags\" introduced function. consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added df. ensures flagged observations WFHZ MFAZ data excluded combined prevalence analysis. table shows overview cflags defined","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"","code":"## When .summary_by and .wt are set to NULL ---- p <- compute_combined_prevalence( df = anthro.02, .wt = NULL, .edema = edema, .summary_by = NULL )  print(p) #> # A tibble: 1 × 16 #>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low #>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl> #> 1    199 0.0685     0.0566     0.0804         Inf     68 0.0129    0.00770 #> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>, #> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>, #> #   wt_pop <dbl>  ## When .wt is not set to NULL ---- x <- compute_combined_prevalence( df = anthro.02, .wt = \"wtfactor\", .edema = edema, .summary_by = NULL )  print(x) #> # A tibble: 1 × 16 #>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low #>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl> #> 1    199 0.0708     0.0563     0.0853        1.72     68 0.0151    0.00750 #> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>, #> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>, #> #   wt_pop <dbl>  ## When working on data frame with multiple survey areas ---- s <- anthro.03 |> mw_wrangle_age( dos = NULL, dob = NULL, age = age, .decimals = 2 ) |> mw_wrangle_muac( sex = sex, muac = muac, age = \"age\", .recode_sex = TRUE, .recode_muac = TRUE, .to = \"cm\" ) |> dplyr::mutate(muac = recode_muac(muac, .to = \"mm\")) |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE) |> compute_combined_prevalence( .edema = edema, .summary_by = district ) #> ================================================================================ #> ================================================================================  print(s) #> # A tibble: 4 × 17 #>   district     cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p #>   <chr>         <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> #> 1 Metuge           15  0.0538    0.0119      0.0956         Inf      6  0.00538 #> 2 Cahora-Bassa     27  0.0836    0.0493      0.118          Inf      2  0       #> 3 Chiuta           11  0.0359    0.00701     0.0647         Inf      4  0.00448 #> 4 Maravia          NA NA        NA          NA               NA     NA NA       #> # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #> #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #> #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"Apply CDC/SMART prevalence weighting approach MUAC data","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"","code":"compute_weighted_prevalence(df, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"df already wrangled dataset object class data.frame use. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"table class data.frame dimensions vary based .summary_by, containing results.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate child's age in months — get_age_months","title":"Calculate child's age in months — get_age_months","text":"Calculate child's age months based date birth date data collection.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate child's age in months — get_age_months","text":"","code":"get_age_months(dos, dob)"},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate child's age in months — get_age_months","text":"dos vector class Date date data collection. class different expected, function stop execution return error message indicating type mismatch. dob vector class Date child's date birth. class different expected, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate child's age in months — get_age_months","text":"vector class numeric child's age months. value less 6.0 greater equal 60.0 months set NA.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate child's age in months — get_age_months","text":"","code":"## Take two vectors of class \"Date\" ---- surv_date <- as.Date(   c(     \"2024-01-05\", \"2024-01-05\", \"2024-01-05\", \"2024-01-08\", \"2024-01-08\",     \"2024-01-08\", \"2024-01-10\", \"2024-01-10\", \"2024-01-10\", \"2024-01-11\"   ) ) bir_date <- as.Date(   c(     \"2022-04-04\", \"2021-05-01\", \"2023-05-24\", \"2017-12-12\", NA,     \"2020-12-12\", \"2022-04-04\", \"2021-05-01\", \"2023-05-24\", \"2020-12-12\"   ) )  ## Apply the function ---- get_age_months(   dos = surv_date,   dob = bir_date ) #>  [1] 21.059548 32.164271  7.425051        NA        NA 36.862423 21.223819 #>  [8] 32.328542  7.589322 36.960986"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample MUAC screening data from an anonymized setting — mfaz.01","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"sample MUAC screening data anonymized setting","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"","code":"mfaz.01"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"tibble 661 rows 4 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"","code":"mfaz.01 #> # A tibble: 667 × 7 #>      sex   age edema  muac age_days   mfaz flag_mfaz #>    <dbl> <dbl> <chr> <dbl>    <dbl>  <dbl>     <dbl> #>  1     2 20.9  n       134     636. -1.11          0 #>  2     2 24.2  n       153     736.  0.331         0 #>  3     2 26.1  n       132     795. -1.62          0 #>  4     1 43.9  n       144    1335. -1.32          0 #>  5     2 25.7  n       150     782  -0.007         0 #>  6     2 39.8  n       174    1210.  1.10          0 #>  7     1 47.9  n       156    1459. -0.412         0 #>  8     2  8.08 n       125     246. -1.37          0 #>  9     2 39.8  n       146    1213. -0.966         0 #> 10     1 47.6  n       150    1450. -0.887         0 #> # ℹ 657 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample SMART survey data with MUAC — mfaz.02","title":"A sample SMART survey data with MUAC — mfaz.02","text":"sample SMART survey data MUAC","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample SMART survey data with MUAC — mfaz.02","text":"","code":"mfaz.02"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample SMART survey data with MUAC — mfaz.02","text":"tibble 303 rows 7 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample SMART survey data with MUAC — mfaz.02","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample SMART survey data with MUAC — mfaz.02","text":"","code":"mfaz.02 #> # A tibble: 303 × 7 #>    cluster   sex   age edema  muac   mfaz flag_mfaz #>      <int> <dbl> <dbl> <chr> <dbl>  <dbl>     <dbl> #>  1       1     1  30.1 n       167  0.957         0 #>  2      11     2   8.8 n       145  0.373         0 #>  3      11     2  43.4 n       150 -0.764         0 #>  4      11     2  39.1 n       168  0.717         0 #>  5       1     1  51.0 n       157 -0.401         0 #>  6       1     2  28.6 n       165  0.977         0 #>  7       1     2  18.8 n       146  0.062         0 #>  8       1     1  55   n       148 -1.20          0 #>  9       1     2  20.3 n       151  0.398         0 #> 10       3     2  58.0 n       155 -0.844         0 #> # ℹ 293 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"Evidence prevalence acute malnutrition used IPC AMN can come different sources: surveys, screenings community-based surveillance system. IPC set minimum sample size requirements source. function helps verifying whether requirements met depending source.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"","code":"mw_check_ipcamn_ssreq(df, cluster, .source = c(\"survey\", \"screening\", \"ssite\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"df dataset object class data.frame check. cluster vector class integer character unique cluster screening sentinel site IDs. character vector, ensure names correct name represents one location accurate counts. class match expected type, function stop execution return error message indicating type mismatch. .source source evidence. choice \"survey\" representative survey data area analysis; \"screening\" screening data; \"ssite\" community-based sentinel site data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"summary table class data.frame, length 3 width 1, check results. n_clusters total number unique clusters screening site IDs; n_obs correspondent total number children dataset; meet_ipc whether IPC AMN requirements met.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"IPC Global Partners. 2021. Integrated Food Security Phase Classification Technical Manual Version 3.1.Evidence Standards Better Food Security Nutrition Decisions. Rome. Available : https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"","code":"mw_check_ipcamn_ssreq(   df = anthro.01,   cluster = cluster,   .source = \"survey\" ) #> # A tibble: 1 × 3 #>   n_clusters n_obs meet_ipc #>        <int> <int> <chr>    #> 1         30  1191 yes"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"Clean format output table returned MFAZ plausibility check improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"","code":"mw_neat_output_mfaz(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"df data frame containing summary table returned package's MFAZ plausibility check function. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle MUAC data ---- data_mfaz <- mw_wrangle_muac(   df = data,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\" ) #> ================================================================================  ## Then run plausibility check ---- pl <- mw_plausibility_check_mfaz(   df = data_mfaz,   flags = flag_mfaz,   sex = sex,   muac = muac,   age = age )  ## Now neat the output table ---- mw_neat_output_mfaz(df = pl) #> # A tibble: 1 × 17 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 0.5%               Excellent                0.297           #> # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #> #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #> #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #> #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #> #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"Clean format output table returned plausibility check raw MUAC data improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"","code":"mw_neat_output_muac(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"df data frame containing summary table returned package's plausibility check function raw MUAC data. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"","code":"## First wranlge MUAC data ---- df_muac <- mw_wrangle_muac(   df = anthro.01,   sex = sex,   muac = muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = FALSE,   .to = \"none\" )  ## Then run the plausibility check ---- pl_muac <- mw_plausibility_check_muac(   df = df_muac,   flags = flag_muac,   sex = sex,   muac = muac )  ## Neat the output table ----  mw_neat_output_muac(df = pl_muac) #> # A tibble: 1 × 9 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 0.3%               Excellent                0.297           #> # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #> #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #> #   `Class. of standard dev` <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"Clean format output table returned WFHZ plausibility check improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"","code":"mw_neat_output_wfhz(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"df data frame containing summary table returned package's WFHZ plausibility check function. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle WFHZ data ---- data_wfhz <- mw_wrangle_wfhz(   df = data,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE ) #> ================================================================================  ## Now run the plausibility check ---- pl <- mw_plausibility_check_wfhz(   df = data_wfhz,   sex = sex,   age = age,   weight = weight,   height = height,   flags = flag_wfhz )  ## Now neat the output table ---- mw_neat_output_wfhz(df = pl) #> # A tibble: 1 × 19 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 1.0%               Excellent                0.297           #> # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #> #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #> #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #> #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #> #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #> #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #> #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, …"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"Check overall plausibility acceptability MFAZ data structured test suite encompassing sampling measurement-related biases checks dataset. test suite function follows recommendation made Bilukha, O., & Kianian, B. (2023) plausibility constructing comprehensive plausibility check similar WFHZ evaluate acceptability MUAC data variable age exists dataset. function works data frame returned package's wrangling function age MFAZ data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"","code":"mw_plausibility_check_mfaz(df, sex, muac, age, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"df dataset object class data.frame check. sex vector class numeric child's sex. muac vector class numeric child's MUAC centimeters. age vector class double child's age months. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"summarised table class data.frame, length 17 width 1, plausibility test results respective acceptability ratings.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle MUAC data ---- data_muac <- mw_wrangle_muac(   df = data,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\" ) #> ================================================================================  ## And finally run plausibility check ---- mw_plausibility_check_mfaz(   df = data_muac,   flags = flag_mfaz,   sex = sex,   muac = muac,   age = age ) #> # A tibble: 1 × 17 #>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio #>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> #> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 #> # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #> #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #> #   kurt_class <fct>, quality_score <dbl>, quality_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"Check overall plausibility acceptability raw MUAC data structured test suite encompassing sampling measurement-related biases checks dataset. test suite function follows recommendation made Bilukha, O., & Kianian, B. (2023).","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"","code":"mw_plausibility_check_muac(df, sex, muac, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"df dataset object class data.frame check. must wrangled using package's wrangling function MUAC. sex vector class numeric child's sex. muac vector class double child's MUAC centimeters. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"summarised table class data.frame, length 9 width 1, plausibility test results respective acceptability ratings..","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"","code":"## First wranlge MUAC data ---- df_muac <- mw_wrangle_muac(   df = anthro.01,   sex = sex,   muac = muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = FALSE,   .to = \"none\" )  ## Then run the plausibility check ---- mw_plausibility_check_muac(   df = df_muac,   flags = flag_muac,   sex = sex,   muac = muac ) #> # A tibble: 1 × 9 #>       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd #>   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> #> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 #> # ℹ 1 more variable: sd_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"Check overall plausibility acceptability WFHZ data structured test suite encompassing sampling measurement-related biases checks dataset. test suite, including criteria corresponding rating acceptability, follows standards  SMART plausibility check. exception exclusion MUAC checks. MUAC checked separately using comprehensive test suite well. function works data frame returned package's wrangling function age WFHZ data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"","code":"mw_plausibility_check_wfhz(df, sex, age, weight, height, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"df dataset object class data.frame check. sex vector class numeric child's sex. age vector class double child's age months. weight vector class double child's weight kilograms. height vector class double child's height centimeters. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"summarised table class data.frame, length 19 width 1, plausibility test results respective acceptability rates.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle WFHZ data ---- data_wfhz <- mw_wrangle_wfhz(   df = data,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE ) #> ================================================================================  ## Now run the plausibility check ---- mw_plausibility_check_wfhz(   df = data_wfhz,   sex = sex,   age = age,   weight = weight,   height = height,   flags = flag_wfhz ) #> # A tibble: 1 × 19 #>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio #>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> #> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 #> # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #> #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #> #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #> #   quality_score <dbl>, quality_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"Calculate observed age ratio children aged 24 59 months old aged 6 23 months old test statistical difference observed expected.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"","code":"mw_stattest_ageratio(age, .expectedP = 0.66)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"age vector class numeric child's age months. different expected, function stop execution return error message indicating type mismatch. .expectedP expected proportion children aged 24 59 months old aged 6 23 months old. estimated 0.66.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"vector class list three statistics: p p-value statistical difference observed expected proportion children aged 24 59 months old aged 6 23 months old; observedR observedP observed ratio proportion respectively.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"function used specifically assessing quality MUAC data. age ratio test children aged 6 29 months old 30 59 months old, performed SMART plausibility check, use nipnTK::ageRatioTest() instead.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"SMART Initiative. Updated MUAC data collection tool. Available : https://smartmethodology.org/survey-planning-tools/updated-muac-tool/","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"","code":"mw_stattest_ageratio(   age = anthro.02$age,   .expectedP = 0.66 ) #> $p #> [1] 0.8669039 #>  #> $observedR #> [1] 1.955671 #>  #> $observedP #> [1] 0.6616674 #>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle child's age — mw_wrangle_age","title":"Wrangle child's age — mw_wrangle_age","text":"Wrangle child's age downstream analysis. includes calculating age months based date data collection child's date birth, setting NA age values less 6.0 greater equal 60.0 months old.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle child's age — mw_wrangle_age","text":"","code":"mw_wrangle_age(df, dos = NULL, dob = NULL, age, .decimals = 2)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle child's age — mw_wrangle_age","text":"df dataset class data.frame wrangle age . dos vector class Date date data collection df. Default NULL. dob vector class Date child's date birth df. Default NULL. age vector class numeric child's age months. cases estimated using local event calendars; cases can mix former one based child's date birth date data collection. .decimals number decimals places age rounded. Default 2.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle child's age — mw_wrangle_age","text":"data.frame based df. variable age automatically filled row age value missing child's date birth date data collection available. Rows age less 6.0 greater equal 60.0 months old set NA. Additionally, new variable df named age_days, class double, created.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle child's age — mw_wrangle_age","text":"","code":"## A sample data ---- df <- data.frame(   surv_date = as.Date(c(     \"2023-01-01\", \"2023-01-01\", \"2023-01-01\", \"2023-01-01\", \"2023-01-01\"   )),   birth_date = as.Date(c(     \"2019-01-01\", NA, \"2018-03-20\", \"2019-11-05\", \"2021-04-25\"   )),   age = c(NA, 36, NA, NA, NA) )  ## Apply the function ---- mw_wrangle_age(   df = df,   dos = surv_date,   dob = birth_date,   age = age,   .decimals = 3 ) #> # A tibble: 5 × 4 #>   surv_date  birth_date   age age_days #>   <date>     <date>     <dbl>    <dbl> #> 1 2023-01-01 2019-01-01  48      1461  #> 2 2023-01-01 NA          36      1096. #> 3 2023-01-01 2018-03-20  57.4    1748  #> 4 2023-01-01 2019-11-05  37.9    1153  #> 5 2023-01-01 2021-04-25  20.2     616"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle MUAC data — mw_wrangle_muac","title":"Wrangle MUAC data — mw_wrangle_muac","text":"Calculate z-scores MUAC--age (MFAZ) identify outliers based SMART methodology. age supplied, wrangling consist detecting outliers raw MUAC values. function works age wrangled.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle MUAC data — mw_wrangle_muac","text":"","code":"mw_wrangle_muac(   df,   sex,   muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = c(\"cm\", \"mm\", \"none\"),   .decimals = 3 )"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle MUAC data — mw_wrangle_muac","text":"df dataset object class data.frame wrangle data . sex numeric character vector child's sex. Code values 1 \"m\" males 2 \"f\" females. Make sure sex values coded either aforementioned calling function. input codes different expected, function stop execution return error message type mismatch. muac vector class numeric child's age months. class different expected, function stop execution return error message indicating type mismatch. age vector class numeric child's age months. .recode_sex Logical. Set TRUE values sex coded 1 (males) 2 (females). Otherwise, set FALSE (default). .recode_muac Logical. Set TRUE values raw MUAC converted either centimeters millimeters. Otherwise, set FALSE (default) .choice measuring unit MUAC values converted; \"cm\" centimeters, \"mm\" millimeters \"none\" leave . .decimals number decimals places z-scores . Default 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle MUAC data — mw_wrangle_muac","text":"data frame based df. New variables named mfaz flag_mfaz, child's MFAZ detected outliers, created. age supplied, flag_muac variable created. refers outliers detected based raw MUAC values.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrangle MUAC data — mw_wrangle_muac","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle MUAC data — mw_wrangle_muac","text":"","code":"## When age is available, wrangle it first before calling the function ---- w <- mw_wrangle_age(   df = anthro.02,   dos = NULL,   dob = NULL,   age = age,   .decimals = 2 )  ### Then apply the function to wrangle MUAC data ---- mw_wrangle_muac(   df = w,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\",   .decimals = 3 ) #> ================================================================================ #> # A tibble: 2,267 × 15 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     2  6.01    8.2   68   n      15.2     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n      13.9     287. -0.006 #>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n      15.5     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n      14.8    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n      13.2     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n      12.9     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n      12.3     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n      12.6     165. -0.349 #>  9 Zambezia Urban      502     2  6.41    7.5   66   n      14.2    1083. -0.006 #> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n      13.5     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, #> #   age_days <dbl>  ## When age is not available ---- mw_wrangle_muac(   df = anthro.02,   sex = sex,   age = NULL,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\",   .decimals = 3 ) #> # A tibble: 2,267 × 15 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     2  6.01    8.2   68   n       152     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n       155     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n       123     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n       126     165. -0.349 #>  9 Zambezia Urban      502     2  6.41    7.5   66   n       142    1083. -0.006 #> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n       135     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, #> #   flag_muac <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle weight-for-height data — mw_wrangle_wfhz","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"Calculate z-scores weight--height (WFHZ) identify outliers based SMART methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"","code":"mw_wrangle_wfhz(df, sex, weight, height, .recode_sex = TRUE, .decimals = 3)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"df dataset object class data.frame wrangle data . sex numeric character vector child's sex. Code values 1 \"m\" males 2 \"f\" females. Make sure sex values coded either aforementioned call function. input codes neither , function stop execution return error message type mismatch. weight vector class double child's weight kilograms. input different class, function stop execution return error message indicating type mismatch. height vector class double child's height centimeters. input different class, function stop execution return error message indicating type mismatch. .recode_sex Logical. Set TRUE values sex coded 1 (males) 2 (females). Otherwise, set FALSE (default). .decimals number decimals places z-scores . Default 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"data frame based df. New variables named wfhz flag_wfhz, child's WFHZ detected outliers, created.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"","code":"mw_wrangle_wfhz(   df = anthro.01,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE,   .decimals = 2 ) #> ================================================================================ #> # A tibble: 1,191 × 13 #>    area      dos        cluster  team   sex dob      age weight height edema #>    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr> #>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n     #>  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n     #>  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n     #>  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n     #>  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n     #>  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n     #>  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n     #>  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n     #>  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n     #> 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n     #> # ℹ 1,181 more rows #> # ℹ 3 more variables: muac <int>, wfhz <dbl>, flag_wfhz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/mwana-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","title":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","text":"streamlined comprehensive implementation Standardized Monitoring Assessment Relief Transition (SMART) Methodology https://smartmethodology.org/ guidelines data quality checks prevalence estimation, enhanced programmable process particularly handling multiple area datasets.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mwana-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","text":"Maintainer: Tomás Zaba tomas.zaba@outlook.com (ORCID) [copyright holder] Authors: Ernest Guevarra (ORCID) [copyright holder]","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify, flag outliers and remove them — flag_outliers","title":"Identify, flag outliers and remove them — flag_outliers","text":"Identify outlier z-scores weight--height (WFHZ) MUAC--age (MFAZ) following SMART methodology. function can also used detect outliers height--age (HFAZ) weight--age (WFAZ) z-scores following approach. raw MUAC values, outliers constitute values less 100 millimeters greater 200 millimeters. Removing outliers consist setting outlier record NA necessarily delete dataset. useful analysis procedures outliers must removed, analysis standard deviation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify, flag outliers and remove them — flag_outliers","text":"","code":"flag_outliers(x, .from = c(\"zscores\", \"raw_muac\"))  remove_flags(x, .from = c(\"zscores\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify, flag outliers and remove them — flag_outliers","text":"x vector class numeric WFHZ, MFAZ, HFAZ, WFAZ raw MUAC values. latter millimeters. class different expected, function stop execution return error message indicating type mismatch. .choice zscores raw_muac outliers detected flagged .","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify, flag outliers and remove them — flag_outliers","text":"vector length x flagged records coded 1 flag 0 flag.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Identify, flag outliers and remove them — flag_outliers","text":"z-score-based detection, flagged records represent outliers deviate substantially sample's z-score mean, making unlikely reflect accurate measurements. raw MUAC values, flagged records fall outside acceptable fixed range. Including outliers analysis compromise accuracy precision resulting estimates. flagging criterion used raw MUAC values based recommendation Bilukha, O., & Kianian, B. (2023).","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Identify, flag outliers and remove them — flag_outliers","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. Available https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify, flag outliers and remove them — flag_outliers","text":"","code":"## Sample data of raw MUAC values ---- x <- anthro.01$muac  ## Apply the function with `.from` set to \"raw_muac\" ---- flag_outliers(x, .from = \"raw_muac\") #>    [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>   [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>   [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [556] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [630] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [667] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [704] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [778] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [815] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [926] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [963] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1000] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1074] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1111] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 #> [1148] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1185] 0 0 0 0 0 0 0  ## Sample data of z-scores (be it WFHZ, MFAZ, HFAZ or WFAZ) ---- x <- anthro.02$mfaz  # Apply the function with `.from` set to \"zscores\" ---- flag_outliers(x, .from = \"zscores\") #>    [1]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 NA #>   [25]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [49]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [73]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [97]  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  1 #>  [121]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [145]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 #>  [169]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [193]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [217]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [241]  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 NA  0  1 #>  [265]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [289]  0  0  0  0  0  0  0  0  0  0  0  1 NA  0  1  0  0  0  1  0  0  0  0  0 #>  [313]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [337]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [361]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [385]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [409]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [433]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [457]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [481]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA #>  [505]  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [529]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 #>  [553]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [577]  0  0  0  0  0  0 NA  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0 #>  [601] NA  0  0  0  0  0 NA  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0 #>  [625]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [649]  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [673]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [697]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [721]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [745] NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [769]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [793]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [817]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [841]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [865]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [889] NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [913]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [937]  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0 #>  [961]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [985]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1009]  0  0  0 NA  0  0  0  0 NA NA  0  0  0  0 NA  0  0  0  0  0  0 NA  0  0 #> [1033]  0  0  0  0  0  0 NA  0  0  0  0  0  0  0 NA NA  0  0  0  0  0  0  0  0 #> [1057]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0 #> [1081]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0 #> [1105]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1129]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1153]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1177]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 NA  0  0  0  0  0  0  0 #> [1201]  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1225]  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0 #> [1249]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1273]  0  0 NA  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0 #> [1297]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1321]  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1345]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1369]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1393]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1417]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1441]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1465]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1489]  0  0  0  0  0 NA  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1513]  0  1  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 #> [1537]  0 NA  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 #> [1561]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0 #> [1585]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1609]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0 NA  0  0  0  0 #> [1633]  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1657]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1681]  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0 #> [1705]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1729]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1753]  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 #> [1777]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1801] NA NA  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1825]  0  0  0  0  1  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1849]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1873]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 NA  0  0  0  0 #> [1897]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 #> [1921]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1945]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1969]  0 NA  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1993]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2017]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2041]  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0 NA  0  0  0  0  0 #> [2065]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2089]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2113]  0  0  0  0  0  0  0  0  0  0 NA  0  0 NA  0  0  0 NA  0  0  0 NA  0  0 #> [2137]  0 NA  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2161]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2185]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2209]  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0 #> [2233]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2257]  0  0  0  0 NA  0  0  0  0  0  0  ## With `.from` set to \"zscores\" ---- remove_flags(   x = wfhz.01$wfhz,   .from = \"zscores\" ) #>   [1]  1.833  0.278 -0.123  1.442  0.652  0.469  0.886 -0.701  0.232 -0.384 #>  [11] -0.124  0.359 -0.888 -0.879 -1.818  0.502  0.862 -0.321  0.016 -0.742 #>  [21] -0.589  1.220 -2.791  0.859 -2.932 -0.479  0.177  1.547  0.491 -0.278 #>  [31] -0.534  0.177 -0.756 -0.221  0.584  0.723  0.560 -1.286 -0.814  0.227 #>  [41] -0.361 -1.154  0.010 -1.170 -0.377 -1.254 -0.244  0.524  0.495  0.488 #>  [51] -2.749 -1.290  0.350 -2.784 -1.402 -0.943  0.333 -0.176 -2.522 -1.870 #>  [61] -2.142     NA -2.173 -0.947     NA -1.256  0.439 -0.169  0.168 -0.839 #>  [71] -0.705 -1.054  1.284  0.064 -0.465  0.445 -0.700  0.728 -1.608 -0.742 #>  [81] -1.406 -0.502 -0.544  0.581 -0.414 -0.397 -0.532 -0.648 -1.776 -2.669 #>  [91] -2.129 -2.404  0.217 -0.947 -0.520 -0.320  0.660  0.757  1.562  0.159 #> [101] -0.686  0.705  0.276 -1.124  0.378 -0.719  0.349 -0.644  0.045  0.350 #> [111]  0.582 -2.617  1.065  0.718  0.200 -0.604  0.597  0.529  0.097 -3.112 #> [121]     NA  0.204 -0.456  0.581 -1.340 -0.950  0.243  0.863     NA -1.050 #> [131]  2.430 -0.314  0.446 -0.303 -0.515  0.402  1.171 -1.577 -1.912 -0.380 #> [141]  0.694 -0.779 -1.794  0.660  1.815  0.376  0.016  0.628 -2.090 -0.198 #> [151] -1.339  0.681 -1.655     NA  0.721 -1.025 -0.912  0.916  1.946  0.074 #> [161]  0.178  0.052  0.131  0.566 -0.409  0.286 -1.999 -0.527  0.977  0.076 #> [171] -0.524  1.346 -0.410 -0.716 -0.271  1.354 -2.332 -0.403 -0.735  1.429 #> [181]  1.090 -0.151 -0.795 -2.563 -0.308 -1.369 -2.206  0.314 -1.107 -0.961 #> [191]  0.500 -1.474 -0.707  0.054 -2.704 -2.215 -0.726 -2.696 -1.320 -0.193 #> [201] -1.129  0.827 -1.533 -1.146 -0.915 -0.858 -1.001 -1.724  1.321  1.095 #> [211]  0.146  0.568  0.039  0.528 -0.236  0.812 -0.877 -0.201 -0.604 -2.323 #> [221]  0.056  0.827  0.557 -1.400  1.174 -0.687 -0.244  0.402  1.929 -0.968 #> [231] -1.391  0.520 -0.901 -1.168 -0.875 -0.350 -0.414 -0.412 -0.296  0.174 #> [241] -0.896 -0.155 -0.808  0.002  0.606 -2.295 -0.058 -0.656 -0.500  0.442 #> [251]  0.122 -0.712 -0.903  0.231 -0.635 -0.238  0.133 -1.232 -0.148 -0.794 #> [261]  0.135  0.401 -0.807  0.472  0.272 -0.662 -0.402 -1.039 -0.076 -0.876 #> [271] -0.703  0.527 -1.335  0.411 -1.861 -1.204 -2.173 -1.044 -0.495 -1.268 #> [281] -1.084 -1.251  0.649 -0.850  1.666  0.545  2.588  0.119 -1.211 -0.489 #> [291]  0.525  0.458  0.083  0.588  1.239 -0.954  0.008  1.269 -0.878  0.222 #> [301] -1.650 -1.103 -0.613  ## With `.from` set to \"raw_muac\" ---- remove_flags(   x = mfaz.01$muac,   .from = \"raw_muac\" ) #>   [1] 134 153 132 144 150 174 156 125 146 150 130 131 146 142 162 145 145 133 #>  [19] 135 111 142 125 165 154 162 150 154 147 161 127 138 175 180 134 154 140 #>  [37] 128 150 153 133 137 140 120 150 150 156 155 152 137 115 130 150 156 140 #>  [55] 133 130 153 120 130 124 153 143 144 160 130 152 130 118 115 146 115 130 #>  [73] 150 150 152 133 140 134 135 144 125 145 137 165 140 140 152 148 158 140 #>  [91] 145 145 140 136 137 136 145 128 145 130 116 152 160 141 112 149 132 130 #> [109] 135 134 130 142 150 138 140 134 131 145 132 113 138 123 129 150 150 136 #> [127] 145 145 130 130 136 183 148 160 132 140 162 128 160 160 143 142 139 135 #> [145] 153 149 147 147 145 134 170 140 170 144 127 125 143 153 142 142 150 160 #> [163] 135 140 145 128 151 114 105 130 100 141 154 125 120 128 152 148 134 158 #> [181] 152 137 127 152 138 155 150 143 121 152 134 135 148 123 120 143 149 151 #> [199] 118 135 121 151 138 135 139 118 145 120 143 148 126 140 128 141 157 116 #> [217] 164 149 145 146 131 149 159 158 131 142 158 140 134 130 144 123 160 140 #> [235] 140 165 130 137 137 124 144 134 153 144 138 130 115 154 136 138 136 138 #> [253] 139 141 132 143 127 145 136 141 144 185 129 121 149 161 117 145 120 180 #> [271] 121 133 131 149 140 139 135 187 159 154 149 141 138 147 133 131 153 145 #> [289] 136 164 149 135 149 137 148 136 147 139 115 149 132 129 160 156 150 150 #> [307] 150 135 150 141 140 150 140 160 150 128 140 122 141 139 142 120 141 113 #> [325] 133 143 122 131 121 121 130 121 117 123 112 138 123 133 133 132 112 132 #> [343] 112 131 122 141 121 121 106 131 141 153 132 149 134 139 131 140 154 152 #> [361] 139 141 131 131 142 151 143 156 142 153 156 142 132 153 150 190 175 135 #> [379] 170 140 140 170 164 151 170 180 148 127 146 145 148 130 163 150 140 145 #> [397] 140 175 190 120 146 159 131 129 158 131 167 156 144 117 144 165 140 165 #> [415] 140 138 158 140 153 137 147 135 175 130 151 138 141 140 182 153 130 130 #> [433] 150 193 120 121 140 137 141 136 146 150 131 123 168 123 132 160 137 182 #> [451] 134 153 130 180 150 143 133 140 129 151 130 148 141 161 149 148 155 139 #> [469] 114 129 162 139 145 132 131 149 136 142 147 137 154 136 138 153 134 142 #> [487] 144 131 145 144 147 128 146 143 145 142 140 161 133 147 117 120 153 161 #> [505] 158 152 143 165 175 138 141 154 149 141 120 148 160 145 160 138 160 135 #> [523] 143 148 153 120 130 149 140 143 156 148 135 144 144 158 160 148 133 142 #> [541] 126 144 129 153 130 161 120 140 135 145 140 139 160 141 160 121 150 168 #> [559] 147 131 135 138 128 140 144 165 145 141 150 136 150 141 149 141 124 125 #> [577] 153 142 140 195 120 123 185 120 143 175 197 179 183 151 142 145 150 143 #> [595] 137 129 139 130 140 138 142 140 151 130 153 156 148 140 158 165 142 140 #> [613] 145 150 124 160 160 158 130 148 120 155 139 141 158 130 135 156 142 143 #> [631] 159 130 160 167 153 155 135 149 142 160 143 157 148 156 153 145 151 138 #> [649] 137 142 139 160 136 152 123 149 130 168 175 161 139 146 143 138 153 158 #> [667] 147"},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"prevalence calculated accordance complex sample design properties inherent surveys. includes weighting survey data applicable applying PROBIT method estimation (WFHZ) standard deviation problematic. SMART Methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"","code":"compute_mfaz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)  compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)  compute_wfhz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"df already wrangled dataset object class data.frame use. .wt vector class double final survey weights. Default NULL assuming self weighted survey, ENA SMART software; otherwise, vector weights supplied, weighted analysis computed. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"summarised table class data.frame descriptive statistics wasting.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"","code":"## An example of application of `compute_muac_prevalence()` ----  ### When .summary.by = NULL ----  x <- compute_muac_prevalence( df = anthro.04, .wt = NULL, .edema = edema, .summary_by = NULL )  print(x) #> # A tibble: 1 × 3 #>    sam_p  mam_p gam_p #>    <dbl>  <dbl> <dbl> #> 1 0.0212 0.0889 0.110  ### When .summary_by is not set to NULL ----  p <- compute_muac_prevalence( df = anthro.04, .wt = NULL, .edema = edema, .summary_by = province )  print(p) #> # A tibble: 3 × 17 #>   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 #> 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA       #> 3 Province 3    NA NA       NA         NA             NA    NA NA       NA       #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>  ## An example of application of `compute_wfhz_prevalence()` ----  ### When .summary_by = NULL ---- anthro.03 |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE ) |> compute_wfhz_prevalence( .wt = NULL, .edema = edema, .summary_by = NULL ) #> ================================================================================ #> # A tibble: 1 × 16 #>   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp #>   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> #> 1    82 0.0768    0.0571    0.0964        Inf    20 0.00973   0.00351    0.0160 #> # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #> #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl>  ### When .summary_by is not set to NULL ----  anthro.03 |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE ) |> compute_wfhz_prevalence( .wt = NULL, .edema = edema, .summary_by = district ) #> ================================================================================ #> # A tibble: 4 × 17 #>   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA       #> 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 #> 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 #> 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA       #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>  ### When a weighted analysis is needed ----  anthro.02 |> compute_wfhz_prevalence( .wt = \"wtfactor\", .edema = edema, .summary_by = province ) #> # A tibble: 2 × 17 #>   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 #> 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272  #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"approach applied standard deviation WFHZ problematic. PROBIT method estimates prevalence wasting indirectly calculating area tail curve, negative infinitive given threshold, using cumulative normal distribution function mean standard deviation inputs.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"","code":"apply_probit_approach(x, .status = c(\"gam\", \"sam\"))  compute_probit_prevalence(df, .summary_by = NULL, .for = c(\"wfhz\", \"mfaz\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"x vector class double WFHZ MFAZ values. .status choice form wasting prevalence estimated. df already wrangled dataset object class data.frame use. .summary_by vector class character geographical areas data collected analysis performed. .choice \"wfhz\" \"mfaz\" anthropometric index.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"summarised table class data.frame prevalence estimates. confidence intervals yielded.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"Rate acceptability age sex ratio test p-values","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"","code":"rate_agesex_ratio(p)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"p vector class double age sex ratio test p-values. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"vector class character length p acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the overall acceptability of the data — rate_overall_quality","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"Rate overall data acceptability score \"Excellent\", \"Good\", \"Acceptable\" \"Problematic\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"","code":"rate_overall_quality(q)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"q vector class numeric integer data acceptability scores. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"vector class factor length q, providing overall rate acceptability data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"Rate acceptability proportion flagged records WFHZ, MFAZ, raw MUAC data following SMART methodology criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"","code":"rate_propof_flagged(p, .in = c(\"mfaz\", \"wfhz\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"p vector class double, containing proportions flagged records dataset. class match expected type, function stop execution return error message indicating type mismatch. .Specifies dataset rating done, options: \"wfhz\", \"mfaz\", \"raw_muac\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"vector class factor length input, acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"Rate acceptability skewness kurtosis test results","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"","code":"rate_skewkurt(sk)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"sk vector class double skewness kurtosis test results. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"vector class factor length sk acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the standard deviation — rate_std","title":"Rate the acceptability of the standard deviation — rate_std","text":"Rate acceptability standard deviation WFHZ, MFAZ, raw MUAC data. Rating follows SMART methodology criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the standard deviation — rate_std","text":"","code":"rate_std(sd, .of = c(\"zscores\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the standard deviation — rate_std","text":"sd vector class double, containing values standard deviation dataset. class match expected type, function stop execution return error message indicating type mismatch. .Specifies dataset rating done, options: \"wfhz\", \"mfaz\", \"raw_muac\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the standard deviation — rate_std","text":"vector class factor length input, acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert MUAC values to either centimeters or millimeters — recode_muac","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"Convert MUAC values either centimeters millimeters required. covert, function checks supplied MUAC values opposite unit intended conversion. , execution stops error message returned.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"","code":"recode_muac(x, .to = c(\"cm\", \"mm\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"x vector raw MUAC values. class can either double numeric integer. different expected, function stop execution return error message indicating type mismatch. .choice cm (centimeters) mm (millimeters) measuring unit convert MUAC values . execute conversion, function checks values opposite unit; case , execution stops error message returned. Strive address error try .","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"numeric vector length x, values converted chosen measuring unit.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"","code":"## Recode from millimeters to centimeters ---- muac_cm <- recode_muac(   x = anthro.01$muac,   .to = \"cm\" )  ## Using the `muac_cm` object to recode it back to \"mm\" ---- muac_mm <- recode_muac(   x = muac_cm,   .to = \"mm\" )"},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"Get overall acceptability score acceptability rate scores","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"","code":"score_overall_quality(   cl_flags,   cl_sex,   cl_age,   cl_dps_m = NULL,   cl_dps_w = NULL,   cl_dps_h = NULL,   cl_std,   cl_skw,   cl_kurt,   .for = c(\"wfhz\", \"mfaz\") )"},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":".choice \"wfhz\" \"mfaz\" basis calculations made.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"vector class numeric, length 1, overall data quality (acceptability) score.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":null,"dir":"Reference","previous_headings":"","what":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"Attribute score, also known penalty point, given rate acceptability standard deviation, proportion flagged records, age sex ratio, skewness, kurtosis digit preference score check results. scoring criteria thresholds follows standards SMART plausibility check.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"","code":"score_std_flags(x)  score_agesexr_dps(x)  score_skewkurt(x)"},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"x vector class character containing acceptability rate given test check. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"vector class integer length x acceptability score.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"determines analysis approach follow given analysis area basis rate acceptability age ratio test standard deviation analysis result.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"","code":"tell_muac_analysis_strategy(age_ratio_class, sd_class)"},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"age_ratio_class vector class character acceptability classification age ratio test result. sd_class vector class character acceptability classification standard deviation analysis result.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"vector class character length input vectors, containing values indicating analysis approach analysis area: \"weighted\", \"unweighted\" \"missing\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"\"weighted\", CDC weighting approach applied correct age bias; \"unweighted\" normal complex sample analysis applied; \"missing\" NA gets thrown.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"sample SMART survey data WFHZ standard deviation rated problematic","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"","code":"wfhz.01"},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"tibble 303 rows 6 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"","code":"wfhz.01 #> # A tibble: 303 × 6 #>    cluster   sex   age edema   wfhz flag_wfhz #>      <int> <dbl> <dbl> <chr>  <dbl>     <dbl> #>  1       1     1  30.1 n      1.83          0 #>  2      11     2   8.8 n      0.278         0 #>  3      11     2  43.4 n     -0.123         0 #>  4      11     2  39.1 n      1.44          0 #>  5       1     1  51.0 n      0.652         0 #>  6       1     2  28.6 n      0.469         0 #>  7       1     2  18.8 n      0.886         0 #>  8       1     1  55   n     -0.701         0 #>  9       1     2  20.3 n      0.232         0 #> 10       3     2  58.0 n     -0.384         0 #> # ℹ 293 more rows"},{"path":"https://nutriverse.io/mwana/dev/news/index.html","id":"mwana-v0109000-development-version","dir":"Changelog","previous_headings":"","what":"mwana v0.1.0.9000 (development version)","title":"mwana v0.1.0.9000 (development version)","text":"Initial pre-release version alpha-testing.","code":""}]
+[{"path":[]},{"path":"https://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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 ernest@guevarra.io. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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://nutriverse.io/mwana/dev/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":[]},{"path":"https://nutriverse.io/mwana/dev/CONTRIBUTING.html","id":"bugs","dir":"","previous_headings":"","what":"Bugs","title":"Contributing","text":"Submit issue issues page","code":""},{"path":"https://nutriverse.io/mwana/dev/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing","text":"Fork repository Github account Clone version account machine account Make sure track progress upstream .e., version mwana nutriverse/mwana, making changes make sure pull changes upstream either git fetch upstream merge later git pull upstream fetch merge one step Make changes new feature branch Please write test tests changes affect code just documentation Push changes account Submit pull request nutriverse/mwana","code":"git clone https://github.com/<yourgithubusername>/mwana.git git remote add upstream https://github.com/nutriverse/mwana.git"},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. 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Patents","title":"GNU General Public License","text":"“contributor” copyright holder authorizes use License Program work Program based. work thus licensed called contributor’s “contributor version”. contributor’s “essential patent claims” patent claims owned controlled contributor, whether already acquired hereafter acquired, infringed manner, permitted License, making, using, selling contributor version, include claims infringed consequence modification contributor version. purposes definition, “control” includes right grant patent sublicenses manner consistent requirements License. contributor grants non-exclusive, worldwide, royalty-free patent license contributor’s essential patent claims, make, use, sell, offer sale, import otherwise run, modify propagate contents contributor version. following three paragraphs, “patent license” express agreement commitment, however denominated, enforce patent (express permission practice patent covenant sue patent infringement). “grant” patent license party means make agreement commitment enforce patent party. convey covered work, knowingly relying patent license, Corresponding Source work available anyone copy, free charge terms License, publicly available network server readily accessible means, must either (1) cause Corresponding Source available, (2) arrange deprive benefit patent license particular work, (3) arrange, manner consistent requirements License, extend patent license downstream recipients. “Knowingly relying” means actual knowledge , patent license, conveying covered work country, recipient’s use covered work country, infringe one identifiable patents country reason believe valid. , pursuant connection single transaction arrangement, convey, propagate procuring conveyance , covered work, grant patent license parties receiving covered work authorizing use, propagate, modify convey specific copy covered work, patent license grant automatically extended recipients covered work works based . patent license “discriminatory” include within scope coverage, prohibits exercise , conditioned non-exercise one rights specifically granted License. may convey covered work party arrangement third party business distributing software, make payment third party based extent activity conveying work, third party grants, parties receive covered work , discriminatory patent license () connection copies covered work conveyed (copies made copies), (b) primarily connection specific products compilations contain covered work, unless entered arrangement, patent license granted, prior 28 March 2007. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. 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Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://nutriverse.io/mwana/dev/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  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This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Running plausibility checks","text":"Plausibility check tool evaluates overall quality acceptability anthropometric data ensure suitability informing decision-making process. mwana provides set handy functions facilitate evaluation. functions allow users assess acceptability weight--height z-score (WFHZ) mid upper-arm circumference (MUAC) data. evaluation latter can done basis MUAC--age z-score (MFAZ) raw MUAC values. vignette, learn use functions consider using MFAZ plausibility check one based raw MUAC values. demonstration, use mwana built-sample dataset named anthro.01. dataset contains district level SMART surveys anonymized locations. ?anthro.01 R console read dataset. begin demonstration plausibility check familiar proceed ones less familiar . check plausibility WFHZ data calling mw_plausibility_check_wfhz() function. , need ensure data right “shape format” accepted understood function. Don’t worry, soon learn get . first, let’s take moment walk key features function. mw_plausibility_check_wfhz() replica plausibility check ENA SMART software SMART Methodology. hood, runs statistical tests already know SMART, applies rating scoring criteria. Beware though small differences mind: mw_plausibility_check_wfhz() include MUAC test suite. simply due fact now can run comprehensive test suite MUAC. mw_plausibility_check_wfhz() allows user run checks multiple area dataset , without repeat workflow number areas data holds. ! Now can begin delving “”. always good practice start inspecting dataset. Let’s check first 6 rows dataset: can see dataset eleven variables, way respective values presented. useful inform data wrangling workflow. mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see : can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 . assess plausibility MUAC data MFAZ age variable available dataset. Note plausibility check MFAZ data built based insights gotten Bilukha, O., & Kianian, B. (2023) research presented 2023 High-Level Technical Assessment Workshop held Nairobi, Kenya. Results research suggested feasibility applying similar plausibility check WFHZ MFAZ, difference amount flags considered: maximum 2%. can run MFAZ plausibility check calling mw_plausibility_check_mfaz(). WFHZ, first need ensure data right shape format accepted understood function. workflow starts processing age; , approach way Section 1.1.1.1. Age ratio test MFAZ know, age ratio test WFHZ done children aged 6 29 months old aged 30 59 months old. different MFAZ. test done children aged 6 23 months aged 24 59 months old. SMART MUAC Tool. test results also used prevalence analysis implement SMART MUAC tool . demonstrated vignette prevalence. job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments. achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨. assess plausibility raw MUAC data ’s raw values variable age available dataset. job assigned mw_plausibility_check_muac(). workflow check shortest one. can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns: just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"head(anthro.01) # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0 x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, … anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-wfhz-data","dir":"Articles","previous_headings":"","what":"Plausibility check of WFHZ data","title":"Running plausibility checks","text":"check plausibility WFHZ data calling mw_plausibility_check_wfhz() function. , need ensure data right “shape format” accepted understood function. Don’t worry, soon learn get . first, let’s take moment walk key features function. mw_plausibility_check_wfhz() replica plausibility check ENA SMART software SMART Methodology. hood, runs statistical tests already know SMART, applies rating scoring criteria. Beware though small differences mind: mw_plausibility_check_wfhz() include MUAC test suite. simply due fact now can run comprehensive test suite MUAC. mw_plausibility_check_wfhz() allows user run checks multiple area dataset , without repeat workflow number areas data holds. ! Now can begin delving “”. always good practice start inspecting dataset. Let’s check first 6 rows dataset: can see dataset eleven variables, way respective values presented. useful inform data wrangling workflow. mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see : can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 .","code":"head(anthro.01) # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0 x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, …"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"data-wrangling","dir":"Articles","previous_headings":"Introduction","what":"Data wrangling","title":"Running plausibility checks","text":"mentioned somewhere , supply data object mw_plausibility_check_wfhz(), need wrangle first. task executed mw_wrangle_age() mw_wrangle_wfhz(). Read technical documentation help(mw_wrangle_age) help(mw_wrangle_wfhz) R console. use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return: , call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see :","code":"age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl> wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"sec-age","dir":"Articles","previous_headings":"Introduction","what":"Age","title":"Running plausibility checks","text":"use mw_wrangle_age() calculate child’s age months based date data collection child’s date birth. done follows: return:","code":"age_mo <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) # A tibble: 6 × 12   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 2 more variables: muac <int>, age_days <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"wrangling-all-other-remaining-variables","dir":"Articles","previous_headings":"Introduction","what":"Wrangling all other remaining variables","title":"Running plausibility checks","text":", call mw_wrangle_wfhz() follows: example, argument .recode_sex set TRUE. hood, compute z-scores, task made possible thanks zscorer package, uses sex coded 1 2 male female, respectively. means sex variable already 1 2’s, set FALSE. Note chance sex variable coded different way aforementioned, recode outside mwana utilities set .recode_sex accordingly. hood, recoding () sex variables, mw_wrangle_wfhz() computes z-scores, identifies outliers adds dataset. Two new variables (wfhz flag_wfhz) created added dataset. can see :","code":"wrangled_df <- anthro.01 |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) ================================================================================ # A tibble: 6 × 3   area         wfhz flag_wfhz   <chr>       <dbl>     <dbl> 1 District E -1.83          0 2 District E -0.956         0 3 District E -0.796         0 4 District E -0.74          0 5 District E -0.679         0 6 District E -0.432         0"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check-of-wfhz-data","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check of WFHZ data","title":"Running plausibility checks","text":"can check plausibility data calling mw_plausibility_check_wfhz() function demonstrated : can chain previous functions way: returned output : can see, returned output summary table statistics ratings. can neat clarity readability. can achieve chaining mw_neat_output_wfhz() previous pipeline: give us: already formatted table, scientific notations converted standard notations, etc. working multiple area dataset, instance districts, can check plausibility districts dataset simply chaining previous workflow group_by() function dplyr package: return following: point, reached end workflow 🎉 .","code":"x <- wrangled_df |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) x <- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) ================================================================================ # A tibble: 1 × 19       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #   quality_score <dbl>, quality_class <fct> anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>   mw_neat_output_wfhz() ================================================================================ # A tibble: 1 × 19   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 1.0%               Excellent                0.297 # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, … ## Load library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = TRUE   ) |>   group_by(area) |>    mw_plausibility_check_wfhz(     sex = sex,     age = age,     weight = weight,     height = height,     flags = flag_wfhz   ) |>    mw_neat_output_wfhz() ================================================================================ # A tibble: 2 × 20   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.8%                     Excellent 2 District G                      686 1.2%                     Excellent # ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS weight (#)` <chr>, #   `Class. DPS weight` <dbl>, `DPS height (#)` <chr>, #   `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>, #   `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, …"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-mfaz-data","dir":"Articles","previous_headings":"","what":"Plausibility check of MFAZ data","title":"Running plausibility checks","text":"assess plausibility MUAC data MFAZ age variable available dataset. Note plausibility check MFAZ data built based insights gotten Bilukha, O., & Kianian, B. (2023) research presented 2023 High-Level Technical Assessment Workshop held Nairobi, Kenya. Results research suggested feasibility applying similar plausibility check WFHZ MFAZ, difference amount flags considered: maximum 2%. can run MFAZ plausibility check calling mw_plausibility_check_mfaz(). WFHZ, first need ensure data right shape format accepted understood function. workflow starts processing age; , approach way Section 1.1.1.1. Age ratio test MFAZ know, age ratio test WFHZ done children aged 6 29 months old aged 30 59 months old. different MFAZ. test done children aged 6 23 months aged 24 59 months old. SMART MUAC Tool. test results also used prevalence analysis implement SMART MUAC tool . demonstrated vignette prevalence. job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments. achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨.","code":"anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"sec-wrangle_mfaz","dir":"Articles","previous_headings":"Introduction","what":"Wrangling MFAZ data","title":"Running plausibility checks","text":"job mw_wrangle_muac() function. use follows: Just WFHZ wrangler, hood, mw_wrangle_muac() computes z-scores identifies outliers flags . stored mfaz flag_mfaz variables created added dataset. code returns: Note mw_wrangle_muac() accepts MUAC values centimeters. takes arguments .recode_muac .control whether need transform variable muacfunction . Read function documentation learn control two arguments.","code":"anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) ================================================================================ # A tibble: 1,191 × 14    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check-of-mfaz-data","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check of MFAZ data","title":"Running plausibility checks","text":"achieve calling mw_plausibility_check_mfaz() function: return: can also neat output. just need call mw_neat_output_mfaz() chain pipeline: return: can also run checks multiple area dataset follows: return: point, reached end workflow ✨.","code":"## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) ================================================================================ # A tibble: 1 × 17       n flagged flagged_class sex_ratio sex_ratio_class age_ratio   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #   kurt_class <fct>, quality_score <dbl>, quality_class <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 1 × 17   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.5%               Excellent                0.297 # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct> ## Load dplyr library ---- library(dplyr)  ## The workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = \"age\",     .recode_sex = TRUE,     .recode_muac = TRUE,     .to = \"cm\"   ) |>   mutate(muac = recode_muac(muac, .to = \"mm\")) |>   group_by(area) |>    mw_plausibility_check_mfaz(     sex = sex,     muac = muac,     age = age,     flags = flag_mfaz   ) |>   mw_neat_output_mfaz() ================================================================================ # A tibble: 2 × 18   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.9%                     Excellent # ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #   `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>, #   `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>, #   `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>, #   `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>, #   `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"plausibility-check-of-raw-muac-data","dir":"Articles","previous_headings":"","what":"Plausibility check of raw MUAC data","title":"Running plausibility checks","text":"assess plausibility raw MUAC data ’s raw values variable age available dataset. job assigned mw_plausibility_check_muac(). workflow check shortest one. can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns: just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"data-wrangling-1","dir":"Articles","previous_headings":"Introduction","what":"Data wrangling","title":"Running plausibility checks","text":"can tell, z-scores computed absence age. way, data wrangling workflow quite minimal. still set arguments inside mw_wrangle_muac() learned Section 1.2.1. difference set age NULL. Fundamentally, hood function detects MUAC values outliers flags stores flag_muac variable added dataset. continue using dataset: returns:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) # A tibble: 1,191 × 12    area      dos        cluster  team   sex dob      age weight height edema    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n # ℹ 1,181 more rows # ℹ 2 more variables: muac <int>, flag_muac <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/plausibility.html","id":"on-to-de-facto-plausibility-check","dir":"Articles","previous_headings":"Introduction","what":"On to de facto plausibility check","title":"Running plausibility checks","text":"just add mw_plausibility_check_muac() pipeline: return: can also return formatted table mw_neat_output_muac(): get working multiple area data, approach task way demonstrated : get:","code":"anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) # A tibble: 1 × 9       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 # ℹ 1 more variable: sd_class <fct> anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 1 × 9   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`              <int> <chr>              <fct>                    <chr> 1             1191 0.3%               Excellent                0.297 # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #   `Class. of standard dev` <fct> ## Load library ---- library(dplyr)  ## Check plausibility ---- anthro.01 |>   mw_wrangle_muac(     sex = sex,     muac = muac,     age = NULL,     .recode_sex = TRUE,     .recode_muac = FALSE,     .to = \"none\"   ) |>   group_by(area) |>   mw_plausibility_check_muac(     sex = sex,     flags = flag_muac,     muac = muac   ) |>   mw_neat_output_muac() # A tibble: 2 × 10   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`   <chr>                         <int> <chr>                    <fct> 1 District E                      505 0.0%                     Excellent 2 District G                      686 0.4%                     Excellent # ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>, #   `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>, #   `Class. of standard dev` <dbl>, `` <fct>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Estimating the prevalence of wasting","text":"vignette demonstrates use mwana package’s functions estimate prevalence wasting. package allow users estimate prevalence based : weight--height z-score (WFHZ) /edema; raw MUAC values /edema; combined prevalence MUAC--age z-score (MFAZ) /edema. prevalence functions mwana carefully conceived designed simplify workflow nutrition data analyst, especially dealing datasets containing imperfections require additional layers analysis. Let’s try clarify two scenarios believe remind complexity involved: analysing multi-area dataset, users likely need estimate prevalence area individually. Afterward, must extract results collate summary table. working MUAC data, age ratio test rated problematic, additional tool required weight prevalence correct age bias. unfortunate cases multiple areas face issue, workflow must repeated several times, making process cumbersome highly error-prone 😬. mwana longer worry 🥳 functions designed deal . demonstrate use, use different datasets containing imperfections alluded : anthro.02 : survey data survey weights. Read data ?anthro.02. anthro.03 : district-level SMART surveys two districts whose WFHZ standard deviation rated problematic rest within range. ?anthro.03 details. anthro.04 : community-based sentinel site data. data different characteristics require different analysis approaches. Now can begin delving function. estimate prevalence wasting based WFHZ use compute_wfhz_prevalence() function. dataset supply must wrangled mw_wrangle_wfhz(). usual, start inspecting dataset: can see dataset contains required variables WFHZ prevalence analysis, including weighted analysis. dataset already wrangled, need call WFHZ wrangler case. begin demonstration unweigthed analysis - typical SMART surveys - proceed weighted analysis. achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics. get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done. prevalence wasting based MFAZ can estimated using compute_mfaz_prevalence() function. function works implemented way demonstrated Section 1.1, exception data wrangling based MUAC. demonstrated plausibility checks. way, avoid redundancy, demonstrate workflow. job assigned compute_muac_prevalence(). call function, starting prevalence estimation, first evaluates acceptability MFAZ standard deviation age ratio test. Yes, read well, MFAZ’s standard deviation, raw values MUAC. Important Although acceptability evaluated basis MFAZ, actual prevalence estimated basis raw MUAC values. MFAZ also used detect outliers flag excluded prevalence analysis. MFAZ standard deviation age ratio test results used control prevalence analysis flow way: MFAZ standard deviation age ratio test problematic, standard complex sample-based prevalence estimated. MFAZ standard deviation problematic age ratio test problematic, CDC/SMART MUAC tool age-weighting approach applied. MFAZ standard deviation problematic, even age ratio problematic, prevalence analysis estimated, instead NA thrown. working multiple-area dataset, conditionals still applied according area’s situation. work multi-area dataset Fundamentally, function performs standard deviation age ratio tests, evaluates acceptability, returns summarized table area. iterates summary table row row checking conditionals. Based conditionals row (area), function accesses original dataset, computes prevalence accordingly, returns results. demonstrate use anthro.04 dataset. usual, let’s first inspect : see data already wrangled, go straight prevalence estimation. Important ENA Software, make sure run plausibility check call prevalence function. good know acceptability data. anthro.04 see province issues, hence expecting see demonstrations based conditionals stated . return: see Province 1, columns filled numbers; Province 2, columns filled numbers, columns filled NAs: age-weighting approach applied. Lastly, Province 3 bunch NA filled everywhere - know 😉 . go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters. estimation combined prevalence wasting task attributed compute_combined_prevalence() function. case-definition based WFHZ, raw MUAC values edema. workflow standpoint, combines workflow demonstrated Section 1.1 Section 1.3. demonstrate ’s implementation use anthro.01 dataset. Let’s inspect data: Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"tail(anthro.02) # A tibble: 6 × 14   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> 1 Nampula  Urban      285     1  59.5   13.8   90.7 n       149     487.  0.689 2 Nampula  Rural      234     1  59.5   17.2  105.  n       193    1045.  0.178 3 Nampula  Rural      263     1  59.6   18.4  100   n       156     952.  2.13 4 Nampula  Rural      257     1  59.7   15.9  100.  n       149     987.  0.353 5 Nampula  Rural      239     1  59.8   12.5   91.5 n       135     663. -0.722 6 Nampula  Rural      263     1  60.0   14.3   93.8 n       142     952.  0.463 # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 8   province   cluster   sex   age  muac edema   mfaz flag_mfaz   <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> 1 Province 3     743     2    21   130 n     -1.50          0 2 Province 3     743     2     9   126 n     -1.33          0 3 Province 3     743     2    12   128 n     -1.27          0 4 Province 3     743     2    34   145 n     -0.839         0 5 Province 3     743     2    11   130 n     -1.04          0 6 Province 3     743     2    33   140 n     -1.23          0 anthro.04 |>   compute_muac_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province   ) # A tibble: 3 × 17   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA 3 Province 3    NA NA       NA         NA             NA    NA NA       NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> ## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> ## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"sec-prevalence-wfhz","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on WFHZ","title":"Estimating the prevalence of wasting","text":"estimate prevalence wasting based WFHZ use compute_wfhz_prevalence() function. dataset supply must wrangled mw_wrangle_wfhz(). usual, start inspecting dataset: can see dataset contains required variables WFHZ prevalence analysis, including weighted analysis. dataset already wrangled, need call WFHZ wrangler case. begin demonstration unweigthed analysis - typical SMART surveys - proceed weighted analysis. achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics. get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done.","code":"tail(anthro.02) # A tibble: 6 × 14   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> 1 Nampula  Urban      285     1  59.5   13.8   90.7 n       149     487.  0.689 2 Nampula  Rural      234     1  59.5   17.2  105.  n       193    1045.  0.178 3 Nampula  Rural      263     1  59.6   18.4  100   n       156     952.  2.13 4 Nampula  Rural      257     1  59.7   15.9  100.  n       149     987.  0.353 5 Nampula  Rural      239     1  59.8   12.5   91.5 n       135     663. -0.722 6 Nampula  Rural      263     1  60.0   14.3   93.8 n       142     952.  0.463 # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-unweighted-prevalence","dir":"Articles","previous_headings":"","what":"Estimation of unweighted prevalence","title":"Estimating the prevalence of wasting","text":"achieve : return: reason variable edema available dataset, ’s plausible, can exclude analysis setting argument .edema NULL: get: inspect gam_n gam_p columns output table previous, notice differences numbers. occurs edema cases excluded second implementation. Note observed change positive cases edema dataset; otherwise, setting .edema = NULL effect whatsoever. output summary show results province. can control using .summary_by argument. examples, set NULL; now let’s pass name column containing locations data collected. case, column province: voila : table two rows returned province’s statistics.","code":"anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> 1   121 0.0408    0.0322    0.0494        Inf    43 0.00664   0.00273    0.0106 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = NULL, # Setting .edema to NULL     .summary_by = NULL   ) # A tibble: 1 × 16   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl> 1   107 0.0342    0.0263    0.0420        Inf    29     0         0         0 # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl> anthro.02 |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province # province is the variable's name holding data on where the survey was conducted.   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0290    0.0195    0.0384        Inf    10 0.00351 0.0000639 2 Nampula     80 0.0546    0.0397    0.0695        Inf    33 0.0103  0.00282 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-weighted-prevalence","dir":"Articles","previous_headings":"","what":"Estimation of weighted prevalence","title":"Estimating the prevalence of wasting","text":"get weighted prevalence, make use .wt argument. pass column name containing final survey weights. case, column name wtfactor. pass quotation \" \": get: work hood compute_wfhz_prevalence() hood, starting prevalence estimation, function first checks quality WFHZ standard deviation. rated problematic, proceeds complex sample-based analysis; otherwise, prevalence estimated applying PROBIT method. see body plausibility report generated ENA. anthro.02 dataset issues, don’t see compute_wfhz_prevalence() action regard. see , let’s use anthro.03 dataset: . anthro.03 contains problematic standard deviation Metuge Maravia districts, remaining districts within range. Let’s inspect dataset: Now let’s apply prevalence function: returned output : Can spot differences? 😎 Yes, ’re absolutely correct! Cahora-Bassa Chiúta districts columns populated numbers, Metuge Maravia, gam_p, sam_p mam_p columns filled numbers, everything else NA. district PROBIT method applied, Cahora-Bassa Chiúta ditricts standard complex sample analysis done.","code":"anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\", # Passing the wtfactor to .wt     .edema = edema,     .summary_by = province   ) anthro.02 |>   compute_wfhz_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> # A tibble: 6 × 9   district cluster  team sex     age weight height edema  muac   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> 1 Metuge         2     2 m      9.99   10.1   69.3 n       172 2 Metuge         2     2 f     43.6    10.9   91.5 n       130 3 Metuge         2     2 f     32.8    11.4   91.4 n       153 4 Metuge         2     2 f      7.62    8.3   69.5 n       133 5 Metuge         2     2 m     28.4    10.7   82.3 n       143 6 Metuge         2     2 f     12.3     6.6   69.4 n       121 anthro.03 |>   mw_wrangle_wfhz(     sex = sex,     .recode_sex = TRUE,     height = height,     weight = weight   ) |>   compute_wfhz_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = district   ) ================================================================================ # A tibble: 4 × 17   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-the-prevalence-of-wasting-based-on-mfaz","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on MFAZ","title":"Estimating the prevalence of wasting","text":"prevalence wasting based MFAZ can estimated using compute_mfaz_prevalence() function. function works implemented way demonstrated Section 1.1, exception data wrangling based MUAC. demonstrated plausibility checks. way, avoid redundancy, demonstrate workflow.","code":""},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"sec-prevalence-muac","dir":"Articles","previous_headings":"","what":"Estimation of the prevalence of wasting based on the raw MUAC values","title":"Estimating the prevalence of wasting","text":"job assigned compute_muac_prevalence(). call function, starting prevalence estimation, first evaluates acceptability MFAZ standard deviation age ratio test. Yes, read well, MFAZ’s standard deviation, raw values MUAC. Important Although acceptability evaluated basis MFAZ, actual prevalence estimated basis raw MUAC values. MFAZ also used detect outliers flag excluded prevalence analysis. MFAZ standard deviation age ratio test results used control prevalence analysis flow way: MFAZ standard deviation age ratio test problematic, standard complex sample-based prevalence estimated. MFAZ standard deviation problematic age ratio test problematic, CDC/SMART MUAC tool age-weighting approach applied. MFAZ standard deviation problematic, even age ratio problematic, prevalence analysis estimated, instead NA thrown. working multiple-area dataset, conditionals still applied according area’s situation. work multi-area dataset Fundamentally, function performs standard deviation age ratio tests, evaluates acceptability, returns summarized table area. iterates summary table row row checking conditionals. Based conditionals row (area), function accesses original dataset, computes prevalence accordingly, returns results. demonstrate use anthro.04 dataset. usual, let’s first inspect : see data already wrangled, go straight prevalence estimation. Important ENA Software, make sure run plausibility check call prevalence function. good know acceptability data. anthro.04 see province issues, hence expecting see demonstrations based conditionals stated . return: see Province 1, columns filled numbers; Province 2, columns filled numbers, columns filled NAs: age-weighting approach applied. Lastly, Province 3 bunch NA filled everywhere - know 😉 . go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters.","code":"# A tibble: 6 × 8   province   cluster   sex   age  muac edema   mfaz flag_mfaz   <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> 1 Province 3     743     2    21   130 n     -1.50          0 2 Province 3     743     2     9   126 n     -1.33          0 3 Province 3     743     2    12   128 n     -1.27          0 4 Province 3     743     2    34   145 n     -0.839         0 5 Province 3     743     2    11   130 n     -1.04          0 6 Province 3     743     2    33   140 n     -1.23          0 anthro.04 |>   compute_muac_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = province   ) # A tibble: 3 × 17   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA 3 Province 3    NA NA       NA         NA             NA    NA NA       NA # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl> ## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-weighted-prevalence-1","dir":"Articles","previous_headings":"","what":"Estimation of weighted prevalence","title":"Estimating the prevalence of wasting","text":"go back anthro.02 dataset. approach task follows: return: Warning may noticed code block, called recode_muac() function inside mutate(). use mw_wrangle_muac(), puts MUAC variable centimeters. compute_muac_prevalence() function defined accept MUAC millimeters. Therefore, must converted millimeters.","code":"## Load library ---- library(dplyr)  ## Compute prevalence ---- anthro.02 |>   mw_wrangle_age(     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = FALSE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   compute_muac_prevalence(     .wt = \"wtfactor\",     .edema = edema,     .summary_by = province   ) ================================================================================ # A tibble: 2 × 17   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl> 1 Nampula     70 0.0571    0.0369    0.0773       2.00    28 0.0196   0.00706 2 Zambezia    65 0.0552    0.0380    0.0725       1.67    18 0.0133   0.00412 # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"estimation-of-the-combined-prevalence-of-wasting","dir":"Articles","previous_headings":"","what":"Estimation of the combined prevalence of wasting","title":"Estimating the prevalence of wasting","text":"estimation combined prevalence wasting task attributed compute_combined_prevalence() function. case-definition based WFHZ, raw MUAC values edema. workflow standpoint, combines workflow demonstrated Section 1.1 Section 1.3. demonstrate ’s implementation use anthro.01 dataset. Let’s inspect data: Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"# A tibble: 6 × 11   area       dos        cluster  team sex   dob      age weight height edema   <chr>      <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> 1 District E 2023-12-04       1     3 m     NA        59   15.6  109.  n 2 District E 2023-12-04       1     3 m     NA         8    7.5   68.6 n 3 District E 2023-12-04       1     3 m     NA        19    9.7   79.5 n 4 District E 2023-12-04       1     3 f     NA        49   14.3  100.  n 5 District E 2023-12-04       1     3 f     NA        32   12.4   92.1 n 6 District E 2023-12-04       1     3 f     NA        17    9.3   77.8 n # ℹ 1 more variable: muac <int> ## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/articles/prevalence.html","id":"data-wrangling","dir":"Articles","previous_headings":"","what":"Data wrangling","title":"Estimating the prevalence of wasting","text":"Fundamentally, combines wrangling workflow WFHZ MUAC data: get wfhz flag_wfhz mfaz flag_mfaz added dataset. output , just selected columns: hood, compute_combined_prevalence() applies analysis approach compute_wfhz_prevalence() compute_muac_prevalence(). checks acceptability standard deviation WFHZ MFAZ age ratio test. following conditionals checked applied: standard deviation WFHZ, MFAZ age ratio test problematic, standard complex sample-based estimation applied. either standard deviation WFHZ MFAZ age ratio test problematic, prevalence computed, NA thrown. function, concept “combined flags” used. combined flag? Combined flags consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added dataset. ensures flagged observations WFHZ MFAZ data excluded prevalence analysis. Table 1: Overview case-definition combined flag Now understand happens hood, can now proceed implement : get : district E NAs returned issues data. leave figure /issue/issues. Tip Consider running plausibility checkers.","code":"## Load library ---- library(dplyr)  ## Apply the wrangling workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     .to = \"cm\",     age = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) ================================================================================ ================================================================================ # A tibble: 1,191 × 5    area         wfhz flag_wfhz   mfaz flag_mfaz    <chr>       <dbl>     <dbl>  <dbl>     <dbl>  1 District E -1.83          0 -1.45          0  2 District E -0.956         0 -1.67          0  3 District E -0.796         0 -0.617         0  4 District E -0.74          0 -1.02          0  5 District E -0.679         0 -0.93          0  6 District E -0.432         0 -1.10          0  7 District E -0.078         0 -0.255         0  8 District E -0.212         0 -0.677         0  9 District E -1.07          0 -2.18          0 10 District E -0.543         0 -0.403         0 # ℹ 1,181 more rows ## Load library ---- library(dplyr)  ## Apply the workflow ---- anthro.01 |>   mw_wrangle_age(     dos = dos,     dob = dob,     age = age,     .decimals = 2   ) |>   mw_wrangle_muac(     sex = sex,     .recode_sex = TRUE,     muac = muac,     .recode_muac = TRUE,     unit = \"cm\",     .to = \"age\"   ) |>   mutate(     muac = recode_muac(muac, .to = \"mm\")   ) |>   mw_wrangle_wfhz(     sex = sex,     weight = weight,     height = height,     .recode_sex = FALSE   ) |>   compute_combined_prevalence(     .wt = NULL,     .edema = edema,     .summary_by = area   ) ================================================================================ ================================================================================ # A tibble: 2 × 17   area       cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p   <chr>       <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> 1 District E     NA NA         NA         NA               NA     NA NA 2 District G     55  0.0703     0.0447     0.0958         Inf     13  0.00747 # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomás Zaba. Author, maintainer, copyright holder. Ernest Guevarra. Author, copyright holder.","code":""},{"path":"https://nutriverse.io/mwana/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Tomás Zaba, Ernest Guevarra (2024). mwana: Utilities Analysing Children's Nutritional Status. R package version 0.0.0.9000, https://github.com/nutriverse/mwana.","code":"@Manual{,   title = {mwana: Utilities for Analysing Children's Nutritional Status},   author = {{Tomás Zaba} and {Ernest Guevarra}},   year = {2024},   note = {R package version 0.0.0.9000},   url = {https://github.com/nutriverse/mwana}, }"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"mwana-utilities-for-analysing-childrens-nutritional-status-","dir":"","previous_headings":"","what":"Utilities for Analysing Children's Nutritional Status","title":"Utilities for Analysing Children's Nutritional Status","text":"Child anthropometric assessments, implemented routinely countries worldwide, cornerstones child nutrition food security surveillance around world. Ensuring quality child anthropometric data, accuracy child undernutrition prevalence estimates, timeliness reporting therefore critical establishing accurate, robust, --date child undernutrition status globally. mwana, term child Elómwè, local language spoken central-northern regions Mozambique, also word similar meaning across Bantu languages (Swahili) spoken many parts Africa, package streamlines child anthropometry data quality checks undernutrition prevalence estimation children 6-59 months old comprehensive implementation SMART Methodology guidelines R.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana borne author’s experience work multiple child anthropometric datasets conduct data quality appraisal prevalence estimation part data quality assurance team Integrated Phase Classification (IPC). current standard child anthropometric data appraisal workflow extremely cumbersome requiring significant time effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment ENA software) step process single dataset. process repeated every dataset needing processed often needing implemented relatively short period time. manual repetitive process, nature, extremely error-prone. mwana, primarily R-based implementation ENA SMART software, simplifies cumbersome workflow programmable process particularly handling large multiple datasets. [!NOTE] mwana made possible thanks state---art work nutrition survey guidance led SMART initiative. hood, mwana bundles SMART guidance use National Information Platforms Nutrition Anthropometric Data Toolkit (nipnTK) functionalities R build handy function around plausibility checks. Click learn nipnTK package.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"what-does-mwana-do","dir":"","previous_headings":"","what":"What does mwana do?","title":"Utilities for Analysing Children's Nutritional Status","text":"automates plausibility checks prevalence analyses respective summaries outputs.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"plausibility-checks","dir":"","previous_headings":"What does mwana do?","what":"Plausibility checks.","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana performs plausibility checks weight--height z-score (WFHZ)-based data mimicking SMART plausibility checkers ENA SMART software, scoring classification criterion. Read guide . performs, well, plausibility checks MUAC data. , mwana integrates recent advances using MUAC--age z-score (MFAZ) assessing plausibility acceptability MUAC data. way, variable age available: mwana performs plausibility checks similar WFHZ, differences scoring criteria flagged data. Otherwise, variables age missing, similar test suit used current version ENA performed. Read guide .","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"prevalence-analysis","dir":"","previous_headings":"What does mwana do?","what":"Prevalence analysis","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana prevalence calculators built take decisions appropriate analysis procedure follow based quality data, per SMART rules. returns output table appropriate results based data quality test results. Fundamentally, calculators loop survey areas dataset whilst performing quality appraisal taking decisions appropriate prevalence analysis procedure follow basis result. mwana computes prevalence : Wasting basis WFHZ /edema; read guide Wasting basis raw MUAC values /edema: , variable age available, mwana applies MFAZ flags, otherwise applies flagging criteria around raw MUAC values, exclude outliers computing prevalence, actual prevalence done raw values; read guide . Wasting basis MFAZ /edema: outliers excluded using MFAZ flags; read guide . Combined prevalence wasting: concept combined flags used streamline flags removed WFHZ MUAC; read guide . mwana provides weighted prevalence analysis, needed. controlled user. possible calculators, including MUAC, combined, currently available ENA SMART. context IPC Acute Malnutrition (IPC AMN) analysis workflow, mwana provides handy function checking minimum sample size requirements given area met basis methodology used collect data: survey, screening sentinel sites. Read guide . [!TIP] undertaking research want wrangle data including statistical models, etc, mwana great helper. [!WARNING] Please note mwana still highly experimental undergoing lot development. Hence, functionalities described high likelihood changing interface approach aim stable working version.","code":""},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Utilities for Analysing Children's Nutritional Status","text":"mwana yet CRAN can installed nutriverse R Universe follows: load memory ","code":"install.packages(   \"mwana\",   repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org') ) library(mwana)"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Utilities for Analysing Children's Nutritional Status","text":"enticed use mwana package found useful, please cite using suggested citation provided call citation function follows:","code":"citation(\"mwana\") #> To cite mwana: in publications use: #>  #>   Tomás Zaba, Ernest Guevarra (2024). _mwana: Utilities for Analysing #>   Children's Nutritional Status_. R package version 0.0.0.9000, #>   <https://github.com/nutriverse/mwana>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {mwana: Utilities for Analysing Children's Nutritional Status}, #>     author = {{Tomás Zaba} and {Ernest Guevarra}}, #>     year = {2024}, #>     note = {R package version 0.0.0.9000}, #>     url = {https://github.com/nutriverse/mwana}, #>   }"},{"path":"https://nutriverse.io/mwana/dev/index.html","id":"community-guidelines","dir":"","previous_headings":"","what":"Community guidelines","title":"Utilities for Analysing Children's Nutritional Status","text":"Feedback, bug reports feature requests welcome; file issues seek support . like contribute package, please see contributing guidelines. project releases Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of district level SMART surveys with location anonymised — anthro.01","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"anthro.01 two-stage cluster-based survey probability selection clusters proportional size population. survey employed SMART methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"","code":"anthro.01"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"tibble 1,191 rows 11 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of district level SMART surveys with location anonymised — anthro.01","text":"","code":"anthro.01 #> # A tibble: 1,191 × 11 #>    area      dos        cluster  team sex   dob      age weight height edema #>    <chr>     <date>       <int> <int> <chr> <date> <int>  <dbl>  <dbl> <chr> #>  1 District… 2023-12-04       1     3 m     NA        59   15.6  109.  n     #>  2 District… 2023-12-04       1     3 m     NA         8    7.5   68.6 n     #>  3 District… 2023-12-04       1     3 m     NA        19    9.7   79.5 n     #>  4 District… 2023-12-04       1     3 f     NA        49   14.3  100.  n     #>  5 District… 2023-12-04       1     3 f     NA        32   12.4   92.1 n     #>  6 District… 2023-12-04       1     3 f     NA        17    9.3   77.8 n     #>  7 District… 2023-12-04       1     3 f     NA        20   10.1   80.4 n     #>  8 District… 2023-12-04       1     3 f     NA        27   11.7   87.1 n     #>  9 District… 2023-12-04       1     3 m     NA        46   13.6   98   n     #> 10 District… 2023-12-04       1     3 m     NA        58   17.2  109.  n     #> # ℹ 1,181 more rows #> # ℹ 1 more variable: muac <int>"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample of an already wrangled survey data — anthro.02","title":"A sample of an already wrangled survey data — anthro.02","text":"household budget survey data conducted Mozambique 2019/2020, known IOF (Inquérito ao Orçamento Familiar Portuguese). IOF two-stage cluster-based survey, representative province level (admin 2), probability selection clusters proportional size population. data collection spans period 12 months.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample of an already wrangled survey data — anthro.02","text":"","code":"anthro.02"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample of an already wrangled survey data — anthro.02","text":"tibble 2,267 rows 14 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample of an already wrangled survey data — anthro.02","text":"Mozambique National Institute Statistics. data publicly available https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-data_access. Data wrangled using package's wranglers. Details survey design can gotten : https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-sampling","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.02.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample of an already wrangled survey data — anthro.02","text":"","code":"anthro.02 #> # A tibble: 2,267 × 14 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #>  3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n       123     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n       126     165. -0.349 #>  9 Zambezia Urban      502     1  6.41    7.5   66   n       142    1083. -0.006 #> 10 Zambezia Urban      500     1  6.41    6.8   64.1 n       135     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"anthro.03 contains survey data four districts. district dataset presents distinct data quality scenarios requires tailored prevalence analysis approach: two districts show problematic WFHZ standard deviation whilst remaining within range. sample data useful demonstrate use prevalence functions multi-area survey data can variations rating acceptability standard deviation, hence require different analyses approaches area ensure accurate estimation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"","code":"anthro.03"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"tibble 943 x 9.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.03.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of district level SMART surveys conducted in Mozambique — anthro.03","text":"","code":"anthro.03 #> # A tibble: 943 × 9 #>    district cluster  team sex     age weight height edema  muac #>    <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int> #>  1 Metuge         2     2 m      9.99   10.1   69.3 n       172 #>  2 Metuge         2     2 f     43.6    10.9   91.5 n       130 #>  3 Metuge         2     2 f     32.8    11.4   91.4 n       153 #>  4 Metuge         2     2 f      7.62    8.3   69.5 n       133 #>  5 Metuge         2     2 m     28.4    10.7   82.3 n       143 #>  6 Metuge         2     2 f     12.3     6.6   69.4 n       121 #>  7 Metuge         2     2 f     32.0    11.1   85.2 n       148 #>  8 Metuge         2     2 m     34.9    12.6   86.5 n       156 #>  9 Metuge         3     3 m      9.07    8.3   71.4 n       145 #> 10 Metuge         3     3 m     45.5    11.5   85.7 n       145 #> # ℹ 933 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"Data generated community-based sentinel site conducted across three provinces. province's dataset presents distinct data quality scenarios, requiring tailored prevalence analysis: \"Province 1\" MFAZ's standard deviation age ratio test rating acceptability falling within range; \"Province 2\" age ratio rated problematic acceptable standard deviation MFAZ; \"Province 3\" tests rated problematic. sample data useful demonstrate use prevalence functions multi-area survey data variations rating acceptability standard deviation exist, hence require different analyses approaches area ensure accurate estimation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"","code":"anthro.04"},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"tibble 3,002 x 8.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/anthro.04.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample data of a community-based sentinel site from an anonymized location — anthro.04","text":"","code":"anthro.04 #> # A tibble: 3,002 × 8 #>    province   cluster   sex   age  muac edema   mfaz flag_mfaz #>    <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl> #>  1 Province 1     298     2    24   136 n     -1.12          0 #>  2 Province 1     298     2    30   116 n     -3.44          0 #>  3 Province 1     298     2     7   140 n      0.084         0 #>  4 Province 1     298     2    18   144 n     -0.068         0 #>  5 Province 1     298     2    10   125 n     -1.48          0 #>  6 Province 1     298     2    11   125 n     -1.52          0 #>  7 Province 1     298     2    30   136 n     -1.46          0 #>  8 Province 1     298     2    24   133 n     -1.40          0 #>  9 Province 1     298     2    10   122 n     -1.78          0 #> 10 Province 1     298     2    24   142 n     -0.579         0 #> # ℹ 2,992 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"Calculate weighted prevalence estimate MUAC adding proportion children 2 years twice proportion children 2 dividing 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"","code":"apply_cdc_age_weighting(muac, age, .edema = NULL, status = c(\"sam\", \"mam\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"muac vector class integer MUAC values (mm). age vector class double child's age months. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. status choice form wasting defined.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"vector class numeric length size 1.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/apply_cdc_age_weighting.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — apply_cdc_age_weighting","text":"function informed output mw_stattest_ageratio().","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":null,"dir":"Reference","previous_headings":"","what":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"Define given observation dataset wasted , basis WFHZ, MFAZ, MUAC combined criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"","code":"define_wasting_cases_muac(muac, edema = NULL, cases = c(\"gam\", \"sam\", \"mam\"))  define_wasting_cases_whz(zscore, edema = NULL, cases = c(\"gam\", \"sam\", \"mam\"))  define_wasting_cases_combined(   zscore,   muac,   edema = NULL,   cases = c(\"cgam\", \"csam\", \"cmam\") )  define_wasting(   df,   zscore = NULL,   muac = NULL,   edema = NULL,   base = c(\"wfhz\", \"muac\", \"combined\") )"},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"muac vector class integer MUAC values millimeters. edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. cases choice form wasting defined. zscore vector class double WFHZ values (3 decimal places). df dataset object class data.frame use. base choice criterion case-definition based.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"vector class numeric dummy values: 1 case 0 case.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"Use define_wasting() add case-definitions data frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/case_definition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria — define_wasting_cases_muac","text":"","code":"## Weight-for-height based case-definition ---- x <- anthro.02 |> define_wasting( zscore = wfhz, edema = edema, base = \"wfhz\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, gam <dbl>, #> #   sam <dbl>, mam <dbl>  ## MUAC-based case-definition ---- x <- anthro.02 |> define_wasting( muac = muac, edema = edema, base = \"muac\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, gam <dbl>, #> #   sam <dbl>, mam <dbl>  ## Combined case-definition ---- x <- anthro.02 |> define_wasting( zscore = wfhz, muac = muac, edema = edema, base = \"combined\" ) head(x) #> # A tibble: 6 × 17 #>   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #> 1 Zambezia Rural      391     1  6.01    8.2   68   n       152     825.  0.349 #> 2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #> 3 Zambezia Rural      399     1  6.11    7.6   64.1 n       155     130.  0.9   #> 4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #> 5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #> 6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #> # ℹ 6 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, cgam <dbl>, #> #   csam <dbl>, cmam <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":null,"dir":"Reference","previous_headings":"","what":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"Classify wasting severe moderate wasting used SMART MUAC tool weighting approach","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"","code":"classify_wasting_for_cdc_approach(muac, .edema = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"muac vector class integer MUAC values millimeters. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/classify_wasting_for_cdc_approach.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach — classify_wasting_for_cdc_approach","text":"vector class character length muac .edema indicating child severe moderately wasted wasted.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"prevalence calculated accordance complex sample design properties inherent surveys. includes weighting survey data applicable. either acceptability standard deviation WFHZ age ratio test problematic, prevalence calculated.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"","code":"compute_pps_based_combined_prevalence(   df,   .wt = NULL,   .edema = NULL,   .summary_by )  compute_combined_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"df already wrangled dataset class data.frame use. wranglers (WFHZ MUAC) need used sequentially, regardless order. Note MUAC values converted millimeters using MUAC wrangler. .wt vector class double final survey weights. Default NULL assuming self-weighted survey, ENA SMART software; otherwise weighted analysis computed. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"summarised table class data.frame descriptive statistics combined wasting.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"concept \"combined flags\" introduced function. consists defining flag observation flagged either flag_wfhz flag_mfaz vectors. new column cflags combined flags created added df. ensures flagged observations WFHZ MFAZ data excluded combined prevalence analysis. table shows overview cflags defined","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/combined_prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute combined prevalence of wasting — compute_pps_based_combined_prevalence","text":"","code":"## When .summary_by and .wt are set to NULL ---- p <- compute_combined_prevalence( df = anthro.02, .wt = NULL, .edema = edema, .summary_by = NULL )  print(p) #> # A tibble: 1 × 16 #>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low #>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl> #> 1    199 0.0685     0.0566     0.0804         Inf     68 0.0129    0.00770 #> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>, #> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>, #> #   wt_pop <dbl>  ## When .wt is not set to NULL ---- x <- compute_combined_prevalence( df = anthro.02, .wt = \"wtfactor\", .edema = edema, .summary_by = NULL )  print(x) #> # A tibble: 1 × 16 #>   cgam_n cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n csam_p csam_p_low #>    <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>  <dbl>      <dbl> #> 1    199 0.0708     0.0563     0.0853        1.72     68 0.0151    0.00750 #> # ℹ 8 more variables: csam_p_upp <dbl>, csam_p_deff <dbl>, cmam_n <dbl>, #> #   cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>, #> #   wt_pop <dbl>  ## When working on data frame with multiple survey areas ---- s <- anthro.03 |> mw_wrangle_age( dos = NULL, dob = NULL, age = age, .decimals = 2 ) |> mw_wrangle_muac( sex = sex, muac = muac, age = \"age\", .recode_sex = TRUE, .recode_muac = TRUE, .to = \"cm\" ) |> dplyr::mutate(muac = recode_muac(muac, .to = \"mm\")) |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE) |> compute_combined_prevalence( .edema = edema, .summary_by = district ) #> ================================================================================ #> ================================================================================  print(s) #> # A tibble: 4 × 17 #>   district     cgam_n  cgam_p cgam_p_low cgam_p_upp cgam_p_deff csam_n   csam_p #>   <chr>         <dbl>   <dbl>      <dbl>      <dbl>       <dbl>  <dbl>    <dbl> #> 1 Metuge           15  0.0538    0.0119      0.0956         Inf      6  0.00538 #> 2 Cahora-Bassa     27  0.0836    0.0493      0.118          Inf      2  0       #> 3 Chiuta           11  0.0359    0.00701     0.0647         Inf      4  0.00448 #> 4 Maravia          NA NA        NA          NA               NA     NA NA       #> # ℹ 9 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>, #> #   cmam_n <dbl>, cmam_p <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, #> #   cmam_p_deff <dbl>, wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"Apply CDC/SMART prevalence weighting approach MUAC data","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"","code":"compute_weighted_prevalence(df, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"df already wrangled dataset object class data.frame use. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/compute_weighted_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply the CDC/SMART prevalence weighting approach on MUAC data — compute_weighted_prevalence","text":"table class data.frame dimensions vary based .summary_by, containing results.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate child's age in months — get_age_months","title":"Calculate child's age in months — get_age_months","text":"Calculate child's age months based date birth date data collection.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate child's age in months — get_age_months","text":"","code":"get_age_months(dos, dob)"},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate child's age in months — get_age_months","text":"dos vector class Date date data collection. class different expected, function stop execution return error message indicating type mismatch. dob vector class Date child's date birth. class different expected, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate child's age in months — get_age_months","text":"vector class numeric child's age months. value less 6.0 greater equal 60.0 months set NA.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/get_age_months.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate child's age in months — get_age_months","text":"","code":"## Take two vectors of class \"Date\" ---- surv_date <- as.Date(   c(     \"2024-01-05\", \"2024-01-05\", \"2024-01-05\", \"2024-01-08\", \"2024-01-08\",     \"2024-01-08\", \"2024-01-10\", \"2024-01-10\", \"2024-01-10\", \"2024-01-11\"   ) ) bir_date <- as.Date(   c(     \"2022-04-04\", \"2021-05-01\", \"2023-05-24\", \"2017-12-12\", NA,     \"2020-12-12\", \"2022-04-04\", \"2021-05-01\", \"2023-05-24\", \"2020-12-12\"   ) )  ## Apply the function ---- get_age_months(   dos = surv_date,   dob = bir_date ) #>  [1] 21.059548 32.164271  7.425051        NA        NA 36.862423 21.223819 #>  [8] 32.328542  7.589322 36.960986"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample MUAC screening data from an anonymized setting — mfaz.01","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"sample MUAC screening data anonymized setting","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"","code":"mfaz.01"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"tibble 661 rows 4 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample MUAC screening data from an anonymized setting — mfaz.01","text":"","code":"mfaz.01 #> # A tibble: 667 × 7 #>      sex   age edema  muac age_days   mfaz flag_mfaz #>    <dbl> <dbl> <chr> <dbl>    <dbl>  <dbl>     <dbl> #>  1     2 20.9  n       134     636. -1.11          0 #>  2     2 24.2  n       153     736.  0.331         0 #>  3     2 26.1  n       132     795. -1.62          0 #>  4     1 43.9  n       144    1335. -1.32          0 #>  5     2 25.7  n       150     782  -0.007         0 #>  6     2 39.8  n       174    1210.  1.10          0 #>  7     1 47.9  n       156    1459. -0.412         0 #>  8     2  8.08 n       125     246. -1.37          0 #>  9     2 39.8  n       146    1213. -0.966         0 #> 10     1 47.6  n       150    1450. -0.887         0 #> # ℹ 657 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample SMART survey data with MUAC — mfaz.02","title":"A sample SMART survey data with MUAC — mfaz.02","text":"sample SMART survey data MUAC","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample SMART survey data with MUAC — mfaz.02","text":"","code":"mfaz.02"},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample SMART survey data with MUAC — mfaz.02","text":"tibble 303 rows 7 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample SMART survey data with MUAC — mfaz.02","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mfaz.02.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample SMART survey data with MUAC — mfaz.02","text":"","code":"mfaz.02 #> # A tibble: 303 × 7 #>    cluster   sex   age edema  muac   mfaz flag_mfaz #>      <int> <dbl> <dbl> <chr> <dbl>  <dbl>     <dbl> #>  1       1     1  30.1 n       167  0.957         0 #>  2      11     2   8.8 n       145  0.373         0 #>  3      11     2  43.4 n       150 -0.764         0 #>  4      11     2  39.1 n       168  0.717         0 #>  5       1     1  51.0 n       157 -0.401         0 #>  6       1     2  28.6 n       165  0.977         0 #>  7       1     2  18.8 n       146  0.062         0 #>  8       1     1  55   n       148 -1.20          0 #>  9       1     2  20.3 n       151  0.398         0 #> 10       3     2  58.0 n       155 -0.844         0 #> # ℹ 293 more rows"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"Evidence prevalence acute malnutrition used IPC AMN can come different sources: surveys, screenings community-based surveillance system. IPC set minimum sample size requirements source. function helps verifying whether requirements met depending source.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"","code":"mw_check_ipcamn_ssreq(df, cluster, .source = c(\"survey\", \"screening\", \"ssite\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"df dataset object class data.frame check. cluster vector class integer character unique cluster screening sentinel site IDs. character vector, ensure names correct name represents one location accurate counts. class match expected type, function stop execution return error message indicating type mismatch. .source source evidence. choice \"survey\" representative survey data area analysis; \"screening\" screening data; \"ssite\" community-based sentinel site data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"summary table class data.frame, length 3 width 1, check results. n_clusters total number unique clusters screening site IDs; n_obs correspondent total number children dataset; meet_ipc whether IPC AMN requirements met.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"IPC Global Partners. 2021. Integrated Food Security Phase Classification Technical Manual Version 3.1.Evidence Standards Better Food Security Nutrition Decisions. Rome. Available : https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_check_ipcamn_ssreq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met — mw_check_ipcamn_ssreq","text":"","code":"mw_check_ipcamn_ssreq(   df = anthro.01,   cluster = cluster,   .source = \"survey\" ) #> # A tibble: 1 × 3 #>   n_clusters n_obs meet_ipc #>        <int> <int> <chr>    #> 1         30  1191 yes"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"Clean format output table returned MFAZ plausibility check improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"","code":"mw_neat_output_mfaz(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"df data frame containing summary table returned package's MFAZ plausibility check function. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_mfaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability — mw_neat_output_mfaz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle MUAC data ---- data_mfaz <- mw_wrangle_muac(   df = data,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\" ) #> ================================================================================  ## Then run plausibility check ---- pl <- mw_plausibility_check_mfaz(   df = data_mfaz,   flags = flag_mfaz,   sex = sex,   muac = muac,   age = age )  ## Now neat the output table ---- mw_neat_output_mfaz(df = pl) #> # A tibble: 1 × 17 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 0.5%               Excellent                0.297           #> # ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #> #   `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>, #> #   `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>, #> #   `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #> #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"Clean format output table returned plausibility check raw MUAC data improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"","code":"mw_neat_output_muac(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"df data frame containing summary table returned package's plausibility check function raw MUAC data. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability. — mw_neat_output_muac","text":"","code":"## First wranlge MUAC data ---- df_muac <- mw_wrangle_muac(   df = anthro.01,   sex = sex,   muac = muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = FALSE,   .to = \"none\" )  ## Then run the plausibility check ---- pl_muac <- mw_plausibility_check_muac(   df = df_muac,   flags = flag_muac,   sex = sex,   muac = muac )  ## Neat the output table ----  mw_neat_output_muac(df = pl_muac) #> # A tibble: 1 × 9 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 0.3%               Excellent                0.297           #> # ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>, #> #   `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>, #> #   `Class. of standard dev` <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"Clean format output table returned WFHZ plausibility check improved clarity readability. converts scientific notations standard notations, round values rename columns meaningful names.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"","code":"mw_neat_output_wfhz(df)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"df data frame containing summary table returned package's WFHZ plausibility check function. Must class data.frame.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"data frame length width df, column names values formatted clarity.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_neat_output_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability — mw_neat_output_wfhz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle WFHZ data ---- data_wfhz <- mw_wrangle_wfhz(   df = data,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE ) #> ================================================================================  ## Now run the plausibility check ---- pl <- mw_plausibility_check_wfhz(   df = data_wfhz,   sex = sex,   age = age,   weight = weight,   height = height,   flags = flag_wfhz )  ## Now neat the output table ---- mw_neat_output_wfhz(df = pl) #> # A tibble: 1 × 19 #>   `Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)` #>              <int> <chr>              <fct>                    <chr>           #> 1             1191 1.0%               Excellent                0.297           #> # ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>, #> #   `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>, #> #   `Class. DPS weight` <chr>, `DPS height (#)` <dbl>, #> #   `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>, #> #   `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>, #> #   `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>, #> #   `Class. of kurtosis` <fct>, `Overall score` <dbl>, …"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"Check overall plausibility acceptability MFAZ data structured test suite encompassing sampling measurement-related biases checks dataset. test suite function follows recommendation made Bilukha, O., & Kianian, B. (2023) plausibility constructing comprehensive plausibility check similar WFHZ evaluate acceptability MUAC data variable age exists dataset. function works data frame returned package's wrangling function age MFAZ data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"","code":"mw_plausibility_check_mfaz(df, sex, muac, age, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"df dataset object class data.frame check. sex vector class numeric child's sex. muac vector class numeric child's MUAC centimeters. age vector class double child's age months. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"summarised table class data.frame, length 17 width 1, plausibility test results respective acceptability ratings.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_mfaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data — mw_plausibility_check_mfaz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle MUAC data ---- data_muac <- mw_wrangle_muac(   df = data,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\" ) #> ================================================================================  ## And finally run plausibility check ---- mw_plausibility_check_mfaz(   df = data_muac,   flags = flag_mfaz,   sex = sex,   muac = muac,   age = age ) #> # A tibble: 1 × 17 #>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio #>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> #> 1  1191 0.00504 Excellent         0.297 Excellent           0.636 #> # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>, #> #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, #> #   kurt_class <fct>, quality_score <dbl>, quality_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"Check overall plausibility acceptability raw MUAC data structured test suite encompassing sampling measurement-related biases checks dataset. test suite function follows recommendation made Bilukha, O., & Kianian, B. (2023).","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"","code":"mw_plausibility_check_muac(df, sex, muac, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"df dataset object class data.frame check. must wrangled using package's wrangling function MUAC. sex vector class numeric child's sex. muac vector class double child's MUAC centimeters. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"summarised table class data.frame, length 9 width 1, plausibility test results respective acceptability ratings..","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of raw MUAC data — mw_plausibility_check_muac","text":"","code":"## First wranlge MUAC data ---- df_muac <- mw_wrangle_muac(   df = anthro.01,   sex = sex,   muac = muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = FALSE,   .to = \"none\" )  ## Then run the plausibility check ---- mw_plausibility_check_muac(   df = df_muac,   flags = flag_muac,   sex = sex,   muac = muac ) #> # A tibble: 1 × 9 #>       n flagged flagged_class sex_ratio sex_ratio_class   dps dps_class    sd #>   <int>   <dbl> <fct>             <dbl> <chr>           <dbl> <chr>     <dbl> #> 1  1191 0.00252 Excellent         0.297 Excellent        5.39 Excellent  11.1 #> # ℹ 1 more variable: sd_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"Check overall plausibility acceptability WFHZ data structured test suite encompassing sampling measurement-related biases checks dataset. test suite, including criteria corresponding rating acceptability, follows standards  SMART plausibility check. exception exclusion MUAC checks. MUAC checked separately using comprehensive test suite well. function works data frame returned package's wrangling function age WFHZ data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"","code":"mw_plausibility_check_wfhz(df, sex, age, weight, height, flags)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"df dataset object class data.frame check. sex vector class numeric child's sex. age vector class double child's age months. weight vector class double child's weight kilograms. height vector class double child's height centimeters. flags vector class numeric flagged records.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"summarised table class data.frame, length 19 width 1, plausibility test results respective acceptability rates.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_plausibility_check_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data — mw_plausibility_check_wfhz","text":"","code":"## First wrangle age data ---- data <- mw_wrangle_age(   df = anthro.01,   dos = dos,   dob = dob,   age = age,   .decimals = 2 )  ## Then wrangle WFHZ data ---- data_wfhz <- mw_wrangle_wfhz(   df = data,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE ) #> ================================================================================  ## Now run the plausibility check ---- mw_plausibility_check_wfhz(   df = data_wfhz,   sex = sex,   age = age,   weight = weight,   height = height,   flags = flag_wfhz ) #> # A tibble: 1 × 19 #>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio #>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl> #> 1  1191  0.0101 Excellent         0.297 Excellent           0.409 #> # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>, #> #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>, #> #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>, #> #   quality_score <dbl>, quality_class <fct>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"Calculate observed age ratio children aged 24 59 months old aged 6 23 months old test statistical difference observed expected.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"","code":"mw_stattest_ageratio(age, .expectedP = 0.66)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"age vector class numeric child's age months. different expected, function stop execution return error message indicating type mismatch. .expectedP expected proportion children aged 24 59 months old aged 6 23 months old. estimated 0.66.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"vector class list three statistics: p p-value statistical difference observed expected proportion children aged 24 59 months old aged 6 23 months old; observedR observedP observed ratio proportion respectively.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"function used specifically assessing quality MUAC data. age ratio test children aged 6 29 months old 30 59 months old, performed SMART plausibility check, use nipnTK::ageRatioTest() instead.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"SMART Initiative. Updated MUAC data collection tool. Available : https://smartmethodology.org/survey-planning-tools/updated-muac-tool/","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_stattest_ageratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old — mw_stattest_ageratio","text":"","code":"mw_stattest_ageratio(   age = anthro.02$age,   .expectedP = 0.66 ) #> $p #> [1] 0.8669039 #>  #> $observedR #> [1] 1.955671 #>  #> $observedP #> [1] 0.6616674 #>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle child's age — mw_wrangle_age","title":"Wrangle child's age — mw_wrangle_age","text":"Wrangle child's age downstream analysis. includes calculating age months based date data collection child's date birth, setting NA age values less 6.0 greater equal 60.0 months old.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle child's age — mw_wrangle_age","text":"","code":"mw_wrangle_age(df, dos = NULL, dob = NULL, age, .decimals = 2)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle child's age — mw_wrangle_age","text":"df dataset class data.frame wrangle age . dos vector class Date date data collection df. Default NULL. dob vector class Date child's date birth df. Default NULL. age vector class numeric child's age months. cases estimated using local event calendars; cases can mix former one based child's date birth date data collection. .decimals number decimals places age rounded. Default 2.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle child's age — mw_wrangle_age","text":"data.frame based df. variable age automatically filled row age value missing child's date birth date data collection available. Rows age less 6.0 greater equal 60.0 months old set NA. Additionally, new variable df named age_days, class double, created.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle child's age — mw_wrangle_age","text":"","code":"## A sample data ---- df <- data.frame(   surv_date = as.Date(c(     \"2023-01-01\", \"2023-01-01\", \"2023-01-01\", \"2023-01-01\", \"2023-01-01\"   )),   birth_date = as.Date(c(     \"2019-01-01\", NA, \"2018-03-20\", \"2019-11-05\", \"2021-04-25\"   )),   age = c(NA, 36, NA, NA, NA) )  ## Apply the function ---- mw_wrangle_age(   df = df,   dos = surv_date,   dob = birth_date,   age = age,   .decimals = 3 ) #> # A tibble: 5 × 4 #>   surv_date  birth_date   age age_days #>   <date>     <date>     <dbl>    <dbl> #> 1 2023-01-01 2019-01-01  48      1461  #> 2 2023-01-01 NA          36      1096. #> 3 2023-01-01 2018-03-20  57.4    1748  #> 4 2023-01-01 2019-11-05  37.9    1153  #> 5 2023-01-01 2021-04-25  20.2     616"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle MUAC data — mw_wrangle_muac","title":"Wrangle MUAC data — mw_wrangle_muac","text":"Calculate z-scores MUAC--age (MFAZ) identify outliers based SMART methodology. age supplied, wrangling consist detecting outliers raw MUAC values. function works age wrangled.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle MUAC data — mw_wrangle_muac","text":"","code":"mw_wrangle_muac(   df,   sex,   muac,   age = NULL,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = c(\"cm\", \"mm\", \"none\"),   .decimals = 3 )"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle MUAC data — mw_wrangle_muac","text":"df dataset object class data.frame wrangle data . sex numeric character vector child's sex. Code values 1 \"m\" males 2 \"f\" females. Make sure sex values coded either aforementioned calling function. input codes different expected, function stop execution return error message type mismatch. muac vector class numeric child's age months. class different expected, function stop execution return error message indicating type mismatch. age vector class numeric child's age months. .recode_sex Logical. Set TRUE values sex coded 1 (males) 2 (females). Otherwise, set FALSE (default). .recode_muac Logical. Set TRUE values raw MUAC converted either centimeters millimeters. Otherwise, set FALSE (default) .choice measuring unit MUAC values converted; \"cm\" centimeters, \"mm\" millimeters \"none\" leave . .decimals number decimals places z-scores . Default 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle MUAC data — mw_wrangle_muac","text":"data frame based df. New variables named mfaz flag_mfaz, child's MFAZ detected outliers, created. age supplied, flag_muac variable created. refers outliers detected based raw MUAC values.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrangle MUAC data — mw_wrangle_muac","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle MUAC data — mw_wrangle_muac","text":"","code":"## When age is available, wrangle it first before calling the function ---- w <- mw_wrangle_age(   df = anthro.02,   dos = NULL,   dob = NULL,   age = age,   .decimals = 2 )  ### Then apply the function to wrangle MUAC data ---- mw_wrangle_muac(   df = w,   sex = sex,   age = age,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\",   .decimals = 3 ) #> ================================================================================ #> # A tibble: 2,267 × 15 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     2  6.01    8.2   68   n      15.2     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n      13.9     287. -0.006 #>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n      15.5     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n      14.8    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n      13.2     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n      12.9     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n      12.3     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n      12.6     165. -0.349 #>  9 Zambezia Urban      502     2  6.41    7.5   66   n      14.2    1083. -0.006 #> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n      13.5     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, #> #   age_days <dbl>  ## When age is not available ---- mw_wrangle_muac(   df = anthro.02,   sex = sex,   age = NULL,   muac = muac,   .recode_sex = TRUE,   .recode_muac = TRUE,   .to = \"cm\",   .decimals = 3 ) #> # A tibble: 2,267 × 15 #>    province strata cluster   sex   age weight height edema  muac wtfactor   wfhz #>    <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl> #>  1 Zambezia Rural      391     2  6.01    8.2   68   n       152     825.  0.349 #>  2 Zambezia Rural      404     2  6.01    7.1   65.1 n       139     287. -0.006 #>  3 Zambezia Rural      399     2  6.11    7.6   64.1 n       155     130.  0.9   #>  4 Zambezia Urban      430     2  6.14    7.9   65.9 n       148    1277.  0.876 #>  5 Zambezia Urban      468     2  6.28    6.6   59.7 n       132     792.  1.38  #>  6 Zambezia Urban      517     2  6.34    6     61.8 n       129     480. -0.583 #>  7 Zambezia Urban      461     2  6.34    6.5   64.4 n       123     977. -0.732 #>  8 Zambezia Rural      382     2  6.41    6.5   63.4 n       126     165. -0.349 #>  9 Zambezia Urban      502     2  6.41    7.5   66   n       142    1083. -0.006 #> 10 Zambezia Urban      500     2  6.41    6.8   64.1 n       135     972. -0.441 #> # ℹ 2,257 more rows #> # ℹ 4 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>, #> #   flag_muac <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrangle weight-for-height data — mw_wrangle_wfhz","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"Calculate z-scores weight--height (WFHZ) identify outliers based SMART methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"","code":"mw_wrangle_wfhz(df, sex, weight, height, .recode_sex = TRUE, .decimals = 3)"},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"df dataset object class data.frame wrangle data . sex numeric character vector child's sex. Code values 1 \"m\" males 2 \"f\" females. Make sure sex values coded either aforementioned call function. input codes neither , function stop execution return error message type mismatch. weight vector class double child's weight kilograms. input different class, function stop execution return error message indicating type mismatch. height vector class double child's height centimeters. input different class, function stop execution return error message indicating type mismatch. .recode_sex Logical. Set TRUE values sex coded 1 (males) 2 (females). Otherwise, set FALSE (default). .decimals number decimals places z-scores . Default 3.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"data frame based df. New variables named wfhz flag_wfhz, child's WFHZ detected outliers, created.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mw_wrangle_wfhz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrangle weight-for-height data — mw_wrangle_wfhz","text":"","code":"mw_wrangle_wfhz(   df = anthro.01,   sex = sex,   weight = weight,   height = height,   .recode_sex = TRUE,   .decimals = 2 ) #> ================================================================================ #> # A tibble: 1,191 × 13 #>    area      dos        cluster  team   sex dob      age weight height edema #>    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr> #>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n     #>  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n     #>  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n     #>  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n     #>  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n     #>  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n     #>  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n     #>  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n     #>  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n     #> 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n     #> # ℹ 1,181 more rows #> # ℹ 3 more variables: muac <int>, wfhz <dbl>, flag_wfhz <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/mwana-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","title":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","text":"streamlined comprehensive implementation Standardized Monitoring Assessment Relief Transition (SMART) Methodology https://smartmethodology.org/ guidelines data quality checks prevalence estimation, enhanced programmable process particularly handling multiple area datasets.","code":""},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/reference/mwana-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mwana: Utilities for Analysing Children's Nutritional Status — mwana-package","text":"Maintainer: Tomás Zaba tomas.zaba@outlook.com (ORCID) [copyright holder] Authors: Ernest Guevarra (ORCID) [copyright holder]","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify, flag outliers and remove them — flag_outliers","title":"Identify, flag outliers and remove them — flag_outliers","text":"Identify outlier z-scores weight--height (WFHZ) MUAC--age (MFAZ) following SMART methodology. function can also used detect outliers height--age (HFAZ) weight--age (WFAZ) z-scores following approach. raw MUAC values, outliers constitute values less 100 millimeters greater 200 millimeters. Removing outliers consist setting outlier record NA necessarily delete dataset. useful analysis procedures outliers must removed, analysis standard deviation.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify, flag outliers and remove them — flag_outliers","text":"","code":"flag_outliers(x, .from = c(\"zscores\", \"raw_muac\"))  remove_flags(x, .from = c(\"zscores\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify, flag outliers and remove them — flag_outliers","text":"x vector class numeric WFHZ, MFAZ, HFAZ, WFAZ raw MUAC values. latter millimeters. class different expected, function stop execution return error message indicating type mismatch. .choice zscores raw_muac outliers detected flagged .","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify, flag outliers and remove them — flag_outliers","text":"vector length x flagged records coded 1 flag 0 flag.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Identify, flag outliers and remove them — flag_outliers","text":"z-score-based detection, flagged records represent outliers deviate substantially sample's z-score mean, making unlikely reflect accurate measurements. raw MUAC values, flagged records fall outside acceptable fixed range. Including outliers analysis compromise accuracy precision resulting estimates. flagging criterion used raw MUAC values based recommendation Bilukha, O., & Kianian, B. (2023).","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Identify, flag outliers and remove them — flag_outliers","text":"Bilukha, O., & Kianian, B. (2023). Considerations assessment measurement quality mid‐upper arm circumference data anthropometric surveys mass nutritional screenings conducted humanitarian refugee settings. Maternal & Child Nutrition, 19, e13478. Available https://doi.org/10.1111/mcn.13478 SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify, flag outliers and remove them — flag_outliers","text":"","code":"## Sample data of raw MUAC values ---- x <- anthro.01$muac  ## Apply the function with `.from` set to \"raw_muac\" ---- flag_outliers(x, .from = \"raw_muac\") #>    [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>   [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>   [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [556] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [630] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [667] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [704] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [778] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [815] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [926] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #>  [963] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1000] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1074] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1111] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 #> [1148] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #> [1185] 0 0 0 0 0 0 0  ## Sample data of z-scores (be it WFHZ, MFAZ, HFAZ or WFAZ) ---- x <- anthro.02$mfaz  # Apply the function with `.from` set to \"zscores\" ---- flag_outliers(x, .from = \"zscores\") #>    [1]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 NA #>   [25]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [49]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [73]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>   [97]  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  1 #>  [121]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [145]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 #>  [169]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [193]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [217]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [241]  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 NA  0  1 #>  [265]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [289]  0  0  0  0  0  0  0  0  0  0  0  1 NA  0  1  0  0  0  1  0  0  0  0  0 #>  [313]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [337]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [361]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [385]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [409]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [433]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [457]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [481]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA #>  [505]  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [529]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 #>  [553]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [577]  0  0  0  0  0  0 NA  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0 #>  [601] NA  0  0  0  0  0 NA  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0 #>  [625]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [649]  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [673]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [697]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [721]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [745] NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [769]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [793]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [817]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [841]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [865]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [889] NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [913]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [937]  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0 #>  [961]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #>  [985]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1009]  0  0  0 NA  0  0  0  0 NA NA  0  0  0  0 NA  0  0  0  0  0  0 NA  0  0 #> [1033]  0  0  0  0  0  0 NA  0  0  0  0  0  0  0 NA NA  0  0  0  0  0  0  0  0 #> [1057]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0 #> [1081]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0 #> [1105]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1129]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1153]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1177]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 NA  0  0  0  0  0  0  0 #> [1201]  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1225]  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0 #> [1249]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1273]  0  0 NA  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0 #> [1297]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1321]  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1345]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1369]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1393]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1417]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1441]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1465]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1489]  0  0  0  0  0 NA  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1513]  0  1  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 #> [1537]  0 NA  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 #> [1561]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0 #> [1585]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1609]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0 NA  0  0  0  0 #> [1633]  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1657]  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1681]  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0 #> [1705]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1729]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1753]  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 #> [1777]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1801] NA NA  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1825]  0  0  0  0  1  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1849]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1873]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0 NA  0  0  0  0 #> [1897]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0 #> [1921]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1945]  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [1969]  0 NA  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0 #> [1993]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2017]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2041]  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0 NA  0  0  0  0  0 #> [2065]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2089]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2113]  0  0  0  0  0  0  0  0  0  0 NA  0  0 NA  0  0  0 NA  0  0  0 NA  0  0 #> [2137]  0 NA  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2161]  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2185]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2209]  0  0  0  0  0  0  0  0  0  0  0  0  0 NA  0  0  0  0  0  0  0  0  0  0 #> [2233]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 #> [2257]  0  0  0  0 NA  0  0  0  0  0  0  ## With `.from` set to \"zscores\" ---- remove_flags(   x = wfhz.01$wfhz,   .from = \"zscores\" ) #>   [1]  1.833  0.278 -0.123  1.442  0.652  0.469  0.886 -0.701  0.232 -0.384 #>  [11] -0.124  0.359 -0.888 -0.879 -1.818  0.502  0.862 -0.321  0.016 -0.742 #>  [21] -0.589  1.220 -2.791  0.859 -2.932 -0.479  0.177  1.547  0.491 -0.278 #>  [31] -0.534  0.177 -0.756 -0.221  0.584  0.723  0.560 -1.286 -0.814  0.227 #>  [41] -0.361 -1.154  0.010 -1.170 -0.377 -1.254 -0.244  0.524  0.495  0.488 #>  [51] -2.749 -1.290  0.350 -2.784 -1.402 -0.943  0.333 -0.176 -2.522 -1.870 #>  [61] -2.142     NA -2.173 -0.947     NA -1.256  0.439 -0.169  0.168 -0.839 #>  [71] -0.705 -1.054  1.284  0.064 -0.465  0.445 -0.700  0.728 -1.608 -0.742 #>  [81] -1.406 -0.502 -0.544  0.581 -0.414 -0.397 -0.532 -0.648 -1.776 -2.669 #>  [91] -2.129 -2.404  0.217 -0.947 -0.520 -0.320  0.660  0.757  1.562  0.159 #> [101] -0.686  0.705  0.276 -1.124  0.378 -0.719  0.349 -0.644  0.045  0.350 #> [111]  0.582 -2.617  1.065  0.718  0.200 -0.604  0.597  0.529  0.097 -3.112 #> [121]     NA  0.204 -0.456  0.581 -1.340 -0.950  0.243  0.863     NA -1.050 #> [131]  2.430 -0.314  0.446 -0.303 -0.515  0.402  1.171 -1.577 -1.912 -0.380 #> [141]  0.694 -0.779 -1.794  0.660  1.815  0.376  0.016  0.628 -2.090 -0.198 #> [151] -1.339  0.681 -1.655     NA  0.721 -1.025 -0.912  0.916  1.946  0.074 #> [161]  0.178  0.052  0.131  0.566 -0.409  0.286 -1.999 -0.527  0.977  0.076 #> [171] -0.524  1.346 -0.410 -0.716 -0.271  1.354 -2.332 -0.403 -0.735  1.429 #> [181]  1.090 -0.151 -0.795 -2.563 -0.308 -1.369 -2.206  0.314 -1.107 -0.961 #> [191]  0.500 -1.474 -0.707  0.054 -2.704 -2.215 -0.726 -2.696 -1.320 -0.193 #> [201] -1.129  0.827 -1.533 -1.146 -0.915 -0.858 -1.001 -1.724  1.321  1.095 #> [211]  0.146  0.568  0.039  0.528 -0.236  0.812 -0.877 -0.201 -0.604 -2.323 #> [221]  0.056  0.827  0.557 -1.400  1.174 -0.687 -0.244  0.402  1.929 -0.968 #> [231] -1.391  0.520 -0.901 -1.168 -0.875 -0.350 -0.414 -0.412 -0.296  0.174 #> [241] -0.896 -0.155 -0.808  0.002  0.606 -2.295 -0.058 -0.656 -0.500  0.442 #> [251]  0.122 -0.712 -0.903  0.231 -0.635 -0.238  0.133 -1.232 -0.148 -0.794 #> [261]  0.135  0.401 -0.807  0.472  0.272 -0.662 -0.402 -1.039 -0.076 -0.876 #> [271] -0.703  0.527 -1.335  0.411 -1.861 -1.204 -2.173 -1.044 -0.495 -1.268 #> [281] -1.084 -1.251  0.649 -0.850  1.666  0.545  2.588  0.119 -1.211 -0.489 #> [291]  0.525  0.458  0.083  0.588  1.239 -0.954  0.008  1.269 -0.878  0.222 #> [301] -1.650 -1.103 -0.613  ## With `.from` set to \"raw_muac\" ---- remove_flags(   x = mfaz.01$muac,   .from = \"raw_muac\" ) #>   [1] 134 153 132 144 150 174 156 125 146 150 130 131 146 142 162 145 145 133 #>  [19] 135 111 142 125 165 154 162 150 154 147 161 127 138 175 180 134 154 140 #>  [37] 128 150 153 133 137 140 120 150 150 156 155 152 137 115 130 150 156 140 #>  [55] 133 130 153 120 130 124 153 143 144 160 130 152 130 118 115 146 115 130 #>  [73] 150 150 152 133 140 134 135 144 125 145 137 165 140 140 152 148 158 140 #>  [91] 145 145 140 136 137 136 145 128 145 130 116 152 160 141 112 149 132 130 #> [109] 135 134 130 142 150 138 140 134 131 145 132 113 138 123 129 150 150 136 #> [127] 145 145 130 130 136 183 148 160 132 140 162 128 160 160 143 142 139 135 #> [145] 153 149 147 147 145 134 170 140 170 144 127 125 143 153 142 142 150 160 #> [163] 135 140 145 128 151 114 105 130 100 141 154 125 120 128 152 148 134 158 #> [181] 152 137 127 152 138 155 150 143 121 152 134 135 148 123 120 143 149 151 #> [199] 118 135 121 151 138 135 139 118 145 120 143 148 126 140 128 141 157 116 #> [217] 164 149 145 146 131 149 159 158 131 142 158 140 134 130 144 123 160 140 #> [235] 140 165 130 137 137 124 144 134 153 144 138 130 115 154 136 138 136 138 #> [253] 139 141 132 143 127 145 136 141 144 185 129 121 149 161 117 145 120 180 #> [271] 121 133 131 149 140 139 135 187 159 154 149 141 138 147 133 131 153 145 #> [289] 136 164 149 135 149 137 148 136 147 139 115 149 132 129 160 156 150 150 #> [307] 150 135 150 141 140 150 140 160 150 128 140 122 141 139 142 120 141 113 #> [325] 133 143 122 131 121 121 130 121 117 123 112 138 123 133 133 132 112 132 #> [343] 112 131 122 141 121 121 106 131 141 153 132 149 134 139 131 140 154 152 #> [361] 139 141 131 131 142 151 143 156 142 153 156 142 132 153 150 190 175 135 #> [379] 170 140 140 170 164 151 170 180 148 127 146 145 148 130 163 150 140 145 #> [397] 140 175 190 120 146 159 131 129 158 131 167 156 144 117 144 165 140 165 #> [415] 140 138 158 140 153 137 147 135 175 130 151 138 141 140 182 153 130 130 #> [433] 150 193 120 121 140 137 141 136 146 150 131 123 168 123 132 160 137 182 #> [451] 134 153 130 180 150 143 133 140 129 151 130 148 141 161 149 148 155 139 #> [469] 114 129 162 139 145 132 131 149 136 142 147 137 154 136 138 153 134 142 #> [487] 144 131 145 144 147 128 146 143 145 142 140 161 133 147 117 120 153 161 #> [505] 158 152 143 165 175 138 141 154 149 141 120 148 160 145 160 138 160 135 #> [523] 143 148 153 120 130 149 140 143 156 148 135 144 144 158 160 148 133 142 #> [541] 126 144 129 153 130 161 120 140 135 145 140 139 160 141 160 121 150 168 #> [559] 147 131 135 138 128 140 144 165 145 141 150 136 150 141 149 141 124 125 #> [577] 153 142 140 195 120 123 185 120 143 175 197 179 183 151 142 145 150 143 #> [595] 137 129 139 130 140 138 142 140 151 130 153 156 148 140 158 165 142 140 #> [613] 145 150 124 160 160 158 130 148 120 155 139 141 158 130 135 156 142 143 #> [631] 159 130 160 167 153 155 135 149 142 160 143 157 148 156 153 145 151 138 #> [649] 137 142 139 160 136 152 123 149 130 168 175 161 139 146 143 138 153 158 #> [667] 147"},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"prevalence calculated accordance complex sample design properties inherent surveys. includes weighting survey data applicable applying PROBIT method estimation (WFHZ) standard deviation problematic. SMART Methodology.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"","code":"compute_mfaz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)  compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)  compute_wfhz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL)"},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"df already wrangled dataset object class data.frame use. .wt vector class double final survey weights. Default NULL assuming self weighted survey, ENA SMART software; otherwise, vector weights supplied, weighted analysis computed. .edema vector class character edema. Code \"y\" presence \"n\" absence bilateral edema. Default NULL. .summary_by vector class character geographical areas data collected analysis performed.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"summarised table class data.frame descriptive statistics wasting.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC — compute_mfaz_prevalence","text":"","code":"## An example of application of `compute_muac_prevalence()` ----  ### When .summary.by = NULL ----  x <- compute_muac_prevalence( df = anthro.04, .wt = NULL, .edema = edema, .summary_by = NULL )  print(x) #> # A tibble: 1 × 3 #>    sam_p  mam_p gam_p #>    <dbl>  <dbl> <dbl> #> 1 0.0212 0.0889 0.110  ### When .summary_by is not set to NULL ----  p <- compute_muac_prevalence( df = anthro.04, .wt = NULL, .edema = edema, .summary_by = province )  print(p) #> # A tibble: 3 × 17 #>   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Province 1   135  0.104    0.0778     0.130        Inf    19  0.0133   0.00682 #> 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA       #> 3 Province 3    NA NA       NA         NA             NA    NA NA       NA       #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>  ## An example of application of `compute_wfhz_prevalence()` ----  ### When .summary_by = NULL ---- anthro.03 |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE ) |> compute_wfhz_prevalence( .wt = NULL, .edema = edema, .summary_by = NULL ) #> ================================================================================ #> # A tibble: 1 × 16 #>   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp #>   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl> #> 1    82 0.0768    0.0571    0.0964        Inf    20 0.00973   0.00351    0.0160 #> # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>, #> #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl>  ### When .summary_by is not set to NULL ----  anthro.03 |> mw_wrangle_wfhz( sex = sex, weight = weight, height = height, .recode_sex = TRUE ) |> compute_wfhz_prevalence( .wt = NULL, .edema = edema, .summary_by = district ) #> ================================================================================ #> # A tibble: 4 × 17 #>   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA       #> 2 Cahora-Ba…    25 0.0738    0.0348    0.113         Inf     4 0.00336  -0.00348 #> 3 Chiuta        11 0.0444    0.0129    0.0759        Inf     2 0.00444  -0.00466 #> 4 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA       #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>  ### When a weighted analysis is needed ----  anthro.02 |> compute_wfhz_prevalence( .wt = \"wtfactor\", .edema = edema, .summary_by = province ) #> # A tibble: 2 × 17 #>   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low #>   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl> #> 1 Zambezia    41 0.0261    0.0161    0.0361       1.16    10 0.00236 -0.000255 #> 2 Nampula     80 0.0595    0.0410    0.0779       1.52    33 0.0129   0.00272  #> # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>, #> #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, #> #   wt_pop <dbl>"},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"approach applied standard deviation WFHZ problematic. PROBIT method estimates prevalence wasting indirectly calculating area tail curve, negative infinitive given threshold, using cumulative normal distribution function mean standard deviation inputs.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"","code":"apply_probit_approach(x, .status = c(\"gam\", \"sam\"))  compute_probit_prevalence(df, .summary_by = NULL, .for = c(\"wfhz\", \"mfaz\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"x vector class double WFHZ MFAZ values. .status choice form wasting prevalence estimated. df already wrangled dataset object class data.frame use. .summary_by vector class character geographical areas data collected analysis performed. .choice \"wfhz\" \"mfaz\" anthropometric index.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/probit-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the prevalence estimates of wasting on the basis of the PROBIT method. — apply_probit_approach","text":"summarised table class data.frame prevalence estimates. confidence intervals yielded.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"Rate acceptability age sex ratio test p-values","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"","code":"rate_agesex_ratio(p)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"p vector class double age sex ratio test p-values. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_agesex_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the age and sex ratio test p-values — rate_agesex_ratio","text":"vector class character length p acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the overall acceptability of the data — rate_overall_quality","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"Rate overall data acceptability score \"Excellent\", \"Good\", \"Acceptable\" \"Problematic\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"","code":"rate_overall_quality(q)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"q vector class numeric integer data acceptability scores. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_overall_quality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the overall acceptability of the data — rate_overall_quality","text":"vector class factor length q, providing overall rate acceptability data.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"Rate acceptability proportion flagged records WFHZ, MFAZ, raw MUAC data following SMART methodology criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"","code":"rate_propof_flagged(p, .in = c(\"mfaz\", \"wfhz\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"p vector class double, containing proportions flagged records dataset. class match expected type, function stop execution return error message indicating type mismatch. .Specifies dataset rating done, options: \"wfhz\", \"mfaz\", \"raw_muac\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_propof_flagged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the proportion of flagged records — rate_propof_flagged","text":"vector class factor length input, acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"Rate acceptability skewness kurtosis test results","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"","code":"rate_skewkurt(sk)"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"sk vector class double skewness kurtosis test results. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_skewkurt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the skewness and kurtosis test results — rate_skewkurt","text":"vector class factor length sk acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate the acceptability of the standard deviation — rate_std","title":"Rate the acceptability of the standard deviation — rate_std","text":"Rate acceptability standard deviation WFHZ, MFAZ, raw MUAC data. Rating follows SMART methodology criteria.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate the acceptability of the standard deviation — rate_std","text":"","code":"rate_std(sd, .of = c(\"zscores\", \"raw_muac\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate the acceptability of the standard deviation — rate_std","text":"sd vector class double, containing values standard deviation dataset. class match expected type, function stop execution return error message indicating type mismatch. .Specifies dataset rating done, options: \"wfhz\", \"mfaz\", \"raw_muac\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/rate_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate the acceptability of the standard deviation — rate_std","text":"vector class factor length input, acceptability rate.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert MUAC values to either centimeters or millimeters — recode_muac","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"Convert MUAC values either centimeters millimeters required. covert, function checks supplied MUAC values opposite unit intended conversion. , execution stops error message returned.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"","code":"recode_muac(x, .to = c(\"cm\", \"mm\"))"},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"x vector raw MUAC values. class can either double numeric integer. different expected, function stop execution return error message indicating type mismatch. .choice cm (centimeters) mm (millimeters) measuring unit convert MUAC values . execute conversion, function checks values opposite unit; case , execution stops error message returned. Strive address error try .","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"numeric vector length x, values converted chosen measuring unit.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/recode_muac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert MUAC values to either centimeters or millimeters — recode_muac","text":"","code":"## Recode from millimeters to centimeters ---- muac_cm <- recode_muac(   x = anthro.01$muac,   .to = \"cm\" )  ## Using the `muac_cm` object to recode it back to \"mm\" ---- muac_mm <- recode_muac(   x = muac_cm,   .to = \"mm\" )"},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"Get overall acceptability score acceptability rate scores","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"","code":"score_overall_quality(   cl_flags,   cl_sex,   cl_age,   cl_dps_m = NULL,   cl_dps_w = NULL,   cl_dps_h = NULL,   cl_std,   cl_skw,   cl_kurt,   .for = c(\"wfhz\", \"mfaz\") )"},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":".choice \"wfhz\" \"mfaz\" basis calculations made.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/score_overall_quality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the overall acceptability score from the acceptability rate scores — score_overall_quality","text":"vector class numeric, length 1, overall data quality (acceptability) score.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":null,"dir":"Reference","previous_headings":"","what":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"Attribute score, also known penalty point, given rate acceptability standard deviation, proportion flagged records, age sex ratio, skewness, kurtosis digit preference score check results. scoring criteria thresholds follows standards SMART plausibility check.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"","code":"score_std_flags(x)  score_agesexr_dps(x)  score_skewkurt(x)"},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"x vector class character containing acceptability rate given test check. class match expected type, function stop execution return error message indicating type mismatch.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"vector class integer length x acceptability score.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/scorer.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Score the acceptability rating of the check results that constitutes the plausibility check suite — score_std_flags","text":"SMART Initiative (2017). Standardized Monitoring Assessment Relief Transition. Manual 2.0. Available : https://smartmethodology.org.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"determines analysis approach follow given analysis area basis rate acceptability age ratio test standard deviation analysis result.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"","code":"tell_muac_analysis_strategy(age_ratio_class, sd_class)"},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"age_ratio_class vector class character acceptability classification age ratio test result. sd_class vector class character acceptability classification standard deviation analysis result.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"vector class character length input vectors, containing values indicating analysis approach analysis area: \"weighted\", \"unweighted\" \"missing\".","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/tell_muac_analysis_strategy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A helper function to determine the MUAC prevalence analysis approach to follow — tell_muac_analysis_strategy","text":"\"weighted\", CDC weighting approach applied correct age bias; \"unweighted\" normal complex sample analysis applied; \"missing\" NA gets thrown.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":null,"dir":"Reference","previous_headings":"","what":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"sample SMART survey data WFHZ standard deviation rated problematic","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"","code":"wfhz.01"},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"tibble 303 rows 6 columns.","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"Anonymous","code":""},{"path":"https://nutriverse.io/mwana/dev/reference/wfhz.01.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sample SMART survey data with WFHZ standard deviation rated as problematic — wfhz.01","text":"","code":"wfhz.01 #> # A tibble: 303 × 6 #>    cluster   sex   age edema   wfhz flag_wfhz #>      <int> <dbl> <dbl> <chr>  <dbl>     <dbl> #>  1       1     1  30.1 n      1.83          0 #>  2      11     2   8.8 n      0.278         0 #>  3      11     2  43.4 n     -0.123         0 #>  4      11     2  39.1 n      1.44          0 #>  5       1     1  51.0 n      0.652         0 #>  6       1     2  28.6 n      0.469         0 #>  7       1     2  18.8 n      0.886         0 #>  8       1     1  55   n     -0.701         0 #>  9       1     2  20.3 n      0.232         0 #> 10       3     2  58.0 n     -0.384         0 #> # ℹ 293 more rows"},{"path":[]},{"path":"https://nutriverse.io/mwana/dev/news/index.html","id":"mwana-v010","dir":"Changelog","previous_headings":"","what":"mwana v0.1.0","title":"mwana v0.1.0","text":"Initial pre-release version alpha-testing.","code":""}]