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paper/paper.Rmd

@@ -28,90 +28,97 @@ journal: JOSS
 
 # Summary
 
-`gglm` implements an interface to produce publication-ready model diagnostic
-plots that complies with the grammar of graphics [@wickham2010]. Further, `gglm`
-utilizes the `broom` and `broom.mixed` R packages to provide support for
-diagnostic plots produced from a variety of model object classes across a wide
-variety of R packages [@broom; @broom.mixed]. A quartet of diagnostic plots can 
-be quickly created using `gglm`'s homonymous function, or plots can be created
-individually through instructive and intuitive layer functions added to a 
-`ggplot2` object [@ggplot2].
+`gglm` implements an interface to produce publication-ready model
+diagnostic plots that complies with the grammar of graphics
+[@wickham2010]. Further, `gglm` utilizes the `broom` and `broom.mixed` R
+packages to provide support for diagnostic plots produced from a variety
+of model object classes across a wide variety of R packages [@broom;
+@broom.mixed]. A quartet of diagnostic plots can be quickly created
+using `gglm`'s homonymous function, or plots can be created individually
+through instructive and intuitive layer functions added to a `ggplot2`
+object [@ggplot2].
 
 # Statement of Need
 
-When scientists, statistical practitioners, students, and others implement
-statistical models, it is of the utmost importance that the modeling assumptions
-are verified through visual diagnostics in order to ensure valid statistical 
-inference. The R statistical software language provides a method for producing
-diagnostic plots for linear model objects created with `stats::lm`, however 
-these plots are visually unappealing, inconsistent with diagnostic plots 
-across other R packages and model types, and out of place in modern statistics
-and data science courses focused on learning R with the `tidyverse` 
-[@tidyverse]. 
+When scientists, statistical practitioners, students, and others
+implement statistical models, it is of the utmost importance that the
+modeling assumptions are verified through visual diagnostics in order to
+ensure valid statistical inference. The R statistical software language
+provides a method for producing diagnostic plots for linear model
+objects created with `stats::lm`, however these plots are visually
+unappealing, inconsistent with diagnostic plots across other R packages
+and model types, and out of place in modern statistics and data science
+courses focused on learning R with the `tidyverse` [@tidyverse].
 
-`gglm` addresses the described issues with current diagnostic plots in R by
-providing a consistent interface for producing beautiful and publication-ready
-diagnostic plots across a large variety of R packages and model types (linear 
-models, linear mixed models, generalized linear mixed models, etc.). `gglm` 
-provides functionality to quickly produce four common diagnostic plots, similar
-to `stats::plot.lm`, but produced by `ggplot2`. Further, `gglm` provides a suite
-of layer functions adhering to the grammar of graphics which allow the user to
-create and fine-tune their diagnostic plots through `ggplot2`'s intuitive 
-interface. The layer functions are particularly applicable in modern courses 
-teaching linear regression where students have already learned `ggplot2`. For 
-example, `gglm` and its layer functions are used in Harvard University's
-introductory statistics course [@mcconville2023]. Outside of educational 
-benefits, `gglm` has potential to allow researchers to more easily publish
-elegant diagnostic plots. `gglm` has been downloaded from CRAN over 23,000 times
-as of January 2024. 
+`gglm` addresses the described issues with current diagnostic plots in R
+by providing a consistent interface for producing beautiful and
+publication-ready diagnostic plots across a large variety of R packages
+and model types (linear models, linear mixed models, generalized linear
+mixed models, etc.). `gglm` provides functionality to quickly produce
+four common diagnostic plots, similar to `stats::plot.lm`, but produced
+by `ggplot2`. Further, `gglm` provides a suite of layer functions
+adhering to the grammar of graphics which allow the user to create and
+fine-tune their diagnostic plots through `ggplot2`'s intuitive
+interface. The layer functions are particularly applicable in modern
+courses teaching linear regression where students have already learned
+`ggplot2`. For example, `gglm` and its layer functions are used in
+Harvard University's introductory statistics course [@mcconville2023].
+Outside of educational benefits, `gglm` has potential to allow
+researchers to more easily publish elegant diagnostic plots. `gglm` has
+been downloaded from CRAN over 23,000 times as of January 2024.
 
 # Usage and Features
 
-`gglm` achieves a balance in functionality by being both as easy to use as the 
-built-in `stats::plot.lm` method, yet still highly intuitive and customizable 
-for the curious user. `gglm` is designed with these traits in mind due to the
-understanding that an individual producing a diagnostic plot will most likely be 
-in one of two camps: 1) the individual who wants an *easy* to use tool that
-allows them to quickly check their model diagnostics, or 2) the individual who
-wants an *intuitive and customizable* tool that allows them to look closely at
-their diagnostics for the purposes of education, fine-tuning graphics for
-publication, or other reasons. `gglm` satisfies the members of both camps.
+`gglm` achieves a balance in functionality by being both as easy to use
+as the built-in `stats::plot.lm` method, yet still highly intuitive and
+customizable for the curious user. `gglm` is designed with these traits
+in mind due to the understanding that an individual producing a
+diagnostic plot will most likely be in one of two camps: 1) the
+individual who wants an *easy* to use tool that allows them to quickly
+check their model diagnostics, or 2) the individual who wants an
+*intuitive and customizable* tool that allows them to look closely at
+their diagnostics for the purposes of education, fine-tuning graphics
+for publication, or other reasons. `gglm` satisfies the members of both
+camps.
 
-The `gglm::gglm` function is made for folks in the first camp who are looking 
-for a more aesthetically pleasing alternative to `stats::plot.lm`. In practice,
-the process of using `gglm::gglm` is as simple as and more general than using 
-`stats::plot.lm`, with steps as follows:
+The `gglm::gglm` function is made for folks in the first camp who are
+looking for a more aesthetically pleasing alternative to
+`stats::plot.lm`. In practice, the process of using `gglm::gglm` is as
+simple as and more general than using `stats::plot.lm`, with steps as
+follows:
 
-+ fit a model of any class listed in `gglm::list_model_classes`,
-+ call `gglm::gglm` on the saved model object. 
+-   fit a model of any class listed in `gglm::list_model_classes`,
+-   call `gglm::gglm` on the saved model object.
 
-The `gglm::stat_*` functions are thus for those in the second camp. `gglm` 
-provides seven functions of this sort, including those that produce the 
-following plots: Cook's distance by leverage, Cook's distance by observation
-number, fitted values by residual values, normal QQ, residual histogram, 
-residual values by leverage, and scale by location. The steps to produce a 
-diagnostic plot with these functions are more fluid than with `gglm::gglm`, 
-but are easy to understand provided the user has an understanding of how to use
-`ggplot2`. One may use the workflow: 
+The `gglm::stat_*` functions are thus for those in the second camp.
+`gglm` provides seven functions of this sort, including those that
+produce the following plots: Cook's distance by leverage, Cook's
+distance by observation number, fitted values by residual values, normal
+QQ, residual histogram, residual values by leverage, and scale by
+location. The steps to produce a diagnostic plot with these functions
+are more fluid than with `gglm::gglm`, but are easy to understand
+provided the user has an understanding of how to use `ggplot2`. One may
+use the workflow:
 
-+ fit a model of any class listed in `gglm::list_model_classes`,
-+ provide the saved model object as data to `ggplot2::ggplot`,
-+ add their intended diagnostic plot layer,
-+ add any more `ggplot2` layers such as themes, labels, annotations, and more to
-create their custom diagnostic plot. 
+-   fit a model of any class listed in `gglm::list_model_classes`,
+-   provide the saved model object as data to `ggplot2::ggplot`,
+-   add their intended diagnostic plot layer,
+-   add any more `ggplot2` layers such as themes, labels, annotations,
+    and more to create their custom diagnostic plot.
 
 # Comparison to Other Packages
 
-Functionality similar to that of `gglm`'s is provided by a variety of R 
-packages. As mentioned throughout, `stats` provides a `plot` method for 
-producing diagnostic plots for `lm` objects with base R graphics [@R]. Further,
-`lindia` produces diagnostic plots for `lm` objects with `ggplot2` graphics, but
-does not include functions that adhere with the grammar of graphics [@lindia]. 
-Finally, many packages provide methods for plotting diagnostics based on their
-own model classes (see, e.g. `lme4::plot.merMod`), however these methods are do
-not have consistent usage across packages [@lme4]. `gglm` hence addresses a
-significant gap in functionality by creating a consistent framework for
-producing diagnostic plots across R packages and model types while adhering to 
-the grammar of graphics. 
+Functionality similar to that of `gglm`'s is provided by a variety of R
+packages. As mentioned throughout, `stats` provides a `plot` method for
+producing diagnostic plots for `lm` objects with base R graphics [@R].
+Further, `lindia` produces diagnostic plots for `lm` objects with
+`ggplot2` graphics, but does not include functions that adhere with the
+grammar of graphics [@lindia]. Finally, many packages provide methods
+for plotting diagnostics based on their own model classes (see, e.g.
+`lme4::plot.merMod`), however these methods are do not have consistent
+usage across packages [@lme4]. `gglm` hence addresses a significant gap
+in functionality by creating a consistent framework for producing
+diagnostic plots across R packages and model types while adhering to the
+grammar of graphics.
 
 # References