diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 08a5ede..51a805e 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -12,7 +12,7 @@ jobs: with: journal: joss # This should be the path to the paper within your repo. - paper-path: paper.md + paper-path: paper/paper.md - name: Upload uses: actions/upload-artifact@v1 with: @@ -20,4 +20,4 @@ jobs: # This is the output path where Pandoc will write the compiled # PDF. Note, this should be the same directory as the input # paper.md - path: paper.pdf \ No newline at end of file + path: paper/paper.pdf \ No newline at end of file diff --git a/paper/apa.csl b/paper/apa.csl deleted file mode 100644 index 32be69e..0000000 --- a/paper/apa.csl +++ /dev/null @@ -1,443 +0,0 @@ - - diff --git a/paper/columns.tex b/paper/columns.tex deleted file mode 100644 index f47d1b1..0000000 --- a/paper/columns.tex +++ /dev/null @@ -1,17 +0,0 @@ -\newenvironment{cols}[1][]{}{} - -\newenvironment{col}[1]{\begin{minipage}{#1}\ignorespaces}{% -\end{minipage} -\ifhmode\unskip\fi -\aftergroup\useignorespacesandallpars} - -\def\useignorespacesandallpars#1\ignorespaces\fi{% -#1\fi\ignorespacesandallpars} - -\makeatletter -\def\ignorespacesandallpars{% - \@ifnextchar\par - {\expandafter\ignorespacesandallpars\@gobble}% - {}% -} -\makeatother \ No newline at end of file diff --git a/paper/paper.Rmd b/paper/paper.Rmd deleted file mode 100644 index 792af3a..0000000 --- a/paper/paper.Rmd +++ /dev/null @@ -1,124 +0,0 @@ ---- -title: 'gglm: An R package implementing the grammar of graphics for linear model diagnostic plots' -tags: - - R - - linear models - - diagnostic plots - - grammar of graphics - - ggplot2 -authors: - - name: Grayson W. White - orcid: 0000-0003-4993-2792 - affiliation: 1 -affiliations: - - name: Michigan State University, Department of Forestry - index: 1 -date: 15 January 2024 -bibliography: paper.bib -citation_author: White -year: 2024 -output: - rticles::joss_article: - keep_tex: true - includes: - in_header: columns.tex -csl: apa.csl -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]. - -# 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]. - -`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. - -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. - -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. - -# 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. - -# References diff --git a/paper/paper.html b/paper/paper.html index 2974a4d..4b8408a 100644 --- a/paper/paper.html +++ b/paper/paper.html @@ -389,27 +389,25 @@

Summary

variety of R packages (Robinson, Hayes, and Couch 2023; Bolker and Robinson 2022). A quartet of diagnostic plots can be quickly created using gglm’s homonymous function, or -through instructive and intuitive layer functions added to a -ggplot2 object (Wickham -2016).

+plots can be created individually through instructive and intuitive +layer functions added to a ggplot2 object (Wickham 2016).

-

Statement of need

+

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 produced for -other R packages and model types, and out of place in modern statistics -and data science courses focused on learning R with the -tidyverse (Wickham et al. -2019).

+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 +(Wickham et al. 2019).

gglm addresses the described issues with current diagnostic plots in R by providing a consistent interface for producing -beautiful and publication-ready diagnostic plots for a large variety of -R packages and model types (linear models, linear mixed models, +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. @@ -418,25 +416,30 @@

Statement of need

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, and in particular they are used in Harvard -University’s introductory statistics course (McConville 2023). 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.

+ggplot2. For example, gglm and its layer +functions are used in Harvard University’s introductory statistics +course (McConville 2023). 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 Philosophy

-

gglm has a simple philosophy for usage of the package: -“be easy, intuitive, and customizable”. This philosophy comes about from -the understanding that an individual producing a diagnostic plot will 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 for publication, or other reasons. -gglm satisfies the individuals in both camps.

+
+

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.

The gglm::gglm function is made for folks in the first -camp who are looking a more aesthetically pleasing alternative to +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:

@@ -445,10 +448,10 @@

Usage and Philosophy

gglm::list_model_classes,
  • call gglm::gglm on the saved model object.
    • -

    The gglm::stat_* functions are thus for the individual -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 +

    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, @@ -472,11 +475,14 @@

    Comparison to Other Packages

    lm objects with base R graphics (R Core Team 2023). Further, lindia produces diagnostic plots for lm objects with ggplot2 graphics, -but does not include functions that adhere with the grammar of graphics. -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 -(Bates et al. 2015).

    +but does not include functions that adhere with the grammar of graphics +(Lee and Ventura 2023). 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 (Bates et al. 2015). 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

    @@ -490,9 +496,13 @@

    References

    Bolker, Ben, and David Robinson. 2022. Broom.mixed: Tidying Methods for Mixed Models. https://CRAN.R-project.org/package=broom.mixed.
    +
    +Lee, Yeuk Yu, and Samuel Ventura. 2023. Lindia: Automated Linear +Regression Diagnostic. https://CRAN.R-project.org/package=lindia. +
    -McConville, Kelly. 2023. “Statistics 100.” Harvard -University Department of Statistics. https://mcconvil.github.io/stat100f23/. +McConville, Kelly. 2023. “STAT 100: Introduction to Statistics and +Data Science.” Harvard University Department of Statistics. https://mcconvil.github.io/stat100f23/.
    R Core Team. 2023. 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\csllabelwidth}{#1}\break} -\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1} - - -\newenvironment{cols}[1][]{}{} - -\newenvironment{col}[1]{\begin{minipage}{#1}\ignorespaces}{% -\end{minipage} -\ifhmode\unskip\fi -\aftergroup\useignorespacesandallpars} - -\def\useignorespacesandallpars#1\ignorespaces\fi{% -#1\fi\ignorespacesandallpars} - -\makeatletter -\def\ignorespacesandallpars{% - \@ifnextchar\par - {\expandafter\ignorespacesandallpars\@gobble}% - {}% -} -\makeatother - -\title{gglm: An R package implementing the grammar of graphics for -linear model diagnostic plots} - - \author[1]{Grayson W. White} - - \affil[1]{Michigan State University, Department of Forestry} - \date{\vspace{-5ex}} - -\begin{document} -\maketitle - -\marginpar{ - %\hrule - \sffamily\small - - {\bfseries DOI:} \href{https://doi.org/}{\color{linky}{}} - - \vspace{2mm} - - {\bfseries Software} - \begin{itemize} - \setlength\itemsep{0em} - \item \href{}{\color{linky}{Review}} \ExternalLink - \item \href{}{\color{linky}{Repository}} \ExternalLink - \item \href{}{\color{linky}{Archive}} \ExternalLink - \end{itemize} - - \vspace{2mm} - - {\bfseries Submitted:} \\ - {\bfseries Published:} - - \vspace{2mm} - {\bfseries License}\\ - Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (\href{http://creativecommons.org/licenses/by/4.0/}{\color{linky}{CC-BY}}). -} - -\hypertarget{summary}{% -\section{Summary}\label{summary}} - -\texttt{gglm} implements an interface to produce publication-ready model -diagnostic plots that complies with the grammar of graphics (Wickham, -2010). Further, \texttt{gglm} utilizes the \texttt{broom} and -\texttt{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 (Bolker \& Robinson, 2022; Robinson, Hayes, \& Couch, 2023). -A quartet of diagnostic plots can be quickly created using -\texttt{gglm}'s homonymous function, or plots can be created -individually through instructive and intuitive layer functions added to -a \texttt{ggplot2} object (Wickham, 2016). - -\hypertarget{statement-of-need}{% -\section{Statement of Need}\label{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 \texttt{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 \texttt{tidyverse} -(Wickham et al., 2019). - -\texttt{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.). \texttt{gglm} provides -functionality to quickly produce four common diagnostic plots, similar -to \texttt{stats::plot.lm}, but produced by \texttt{ggplot2}. Further, -\texttt{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 \texttt{ggplot2}'s intuitive interface. The -layer functions are particularly applicable in modern courses teaching -linear regression where students have already learned \texttt{ggplot2}. -For example, \texttt{gglm} and its layer functions are used in Harvard -University's introductory statistics course (McConville, 2023). Outside -of educational benefits, \texttt{gglm} has potential to allow -researchers to more easily publish elegant diagnostic plots. -\texttt{gglm} has been downloaded from CRAN over 23,000 times as of -January 2024. - -\hypertarget{usage-and-features}{% -\section{Usage and Features}\label{usage-and-features}} - -\texttt{gglm} achieves a balance in functionality by being both as easy -to use as the built-in \texttt{stats::plot.lm} method, yet still highly -intuitive and customizable for the curious user. \texttt{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 \emph{easy} to use tool that -allows them to quickly check their model diagnostics, or 2) the -individual who wants an \emph{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. -\texttt{gglm} satisfies the members of both camps. - -The \texttt{gglm::gglm} function is made for folks in the first camp who -are looking for a more aesthetically pleasing alternative to -\texttt{stats::plot.lm}. In practice, the process of using -\texttt{gglm::gglm} is as simple as and more general than using -\texttt{stats::plot.lm}, with steps as follows: - -\begin{itemize} -\tightlist -\item - fit a model of any class listed in - \texttt{gglm::list\_model\_classes}, -\item - call \texttt{gglm::gglm} on the saved model object. -\end{itemize} - -The \texttt{gglm::stat\_*} functions are thus for those in the second -camp. \texttt{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 \texttt{gglm::gglm}, but are easy to understand -provided the user has an understanding of how to use \texttt{ggplot2}. -One may use the workflow: - -\begin{itemize} -\tightlist -\item - fit a model of any class listed in - \texttt{gglm::list\_model\_classes}, -\item - provide the saved model object as data to \texttt{ggplot2::ggplot}, -\item - add their intended diagnostic plot layer, -\item - add any more \texttt{ggplot2} layers such as themes, labels, - annotations, and more to create their custom diagnostic plot. -\end{itemize} - -\hypertarget{comparison-to-other-packages}{% -\section{Comparison to Other -Packages}\label{comparison-to-other-packages}} - -Functionality similar to that of \texttt{gglm}'s is provided by a -variety of R packages. As mentioned throughout, \texttt{stats} provides -a \texttt{plot} method for producing diagnostic plots for \texttt{lm} -objects with base R graphics (R Core Team, 2023). Further, -\texttt{lindia} produces diagnostic plots for \texttt{lm} objects with -\texttt{ggplot2} graphics, but does not include functions that adhere -with the grammar of graphics (Lee \& Ventura, 2023). Finally, many -packages provide methods for plotting diagnostics based on their own -model classes (see, e.g. \texttt{lme4::plot.merMod}), however these -methods are do not have consistent usage across packages (Bates, -Mächler, Bolker, \& Walker, 2015). \texttt{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. - -\hypertarget{references}{% -\section*{References}\label{references}} -\addcontentsline{toc}{section}{References} - -\hypertarget{refs}{} -\begin{CSLReferences}{1}{0} -\leavevmode\vadjust pre{\hypertarget{ref-lme4}{}}% -Bates, D., Mächler, M., Bolker, B., \& Walker, S. (2015). Fitting linear -mixed-effects models using {lme4}. \emph{Journal of Statistical -Software}, \emph{67}(1), 1--48. -doi:\href{https://doi.org/10.18637/jss.v067.i01}{10.18637/jss.v067.i01} - -\leavevmode\vadjust pre{\hypertarget{ref-broom.mixed}{}}% -Bolker, B., \& Robinson, D. (2022). \emph{Broom.mixed: Tidying methods -for mixed models}. Retrieved from -\url{https://CRAN.R-project.org/package=broom.mixed} - -\leavevmode\vadjust pre{\hypertarget{ref-lindia}{}}% -Lee, Y. Y., \& Ventura, S. (2023). \emph{Lindia: Automated linear -regression diagnostic}. Retrieved from -\url{https://CRAN.R-project.org/package=lindia} - -\leavevmode\vadjust pre{\hypertarget{ref-mcconville2023}{}}% -McConville, K. (2023). STAT 100: Introduction to statistics and data -science. Harvard University Department of Statistics. Retrieved from -\url{https://mcconvil.github.io/stat100f23/} - -\leavevmode\vadjust pre{\hypertarget{ref-R}{}}% -R Core Team. (2023). \emph{R: A language and environment for statistical -computing}. Vienna, Austria: R Foundation for Statistical Computing. -Retrieved from \url{https://www.R-project.org/} - -\leavevmode\vadjust pre{\hypertarget{ref-broom}{}}% -Robinson, D., Hayes, A., \& Couch, S. (2023). \emph{Broom: Convert -statistical objects into tidy tibbles}. Retrieved from -\url{https://CRAN.R-project.org/package=broom} - -\leavevmode\vadjust pre{\hypertarget{ref-wickham2010}{}}% -Wickham, H. (2010). A layered grammar of graphics. \emph{Journal of -Computational and Graphical Statistics}, \emph{19}(1), 3--28. -doi:\href{https://doi.org/10.1198/jcgs.2009.07098}{10.1198/jcgs.2009.07098} - -\leavevmode\vadjust pre{\hypertarget{ref-ggplot2}{}}% -Wickham, H. (2016). \emph{ggplot2: Elegant graphics for data analysis}. -Springer-Verlag New York. Retrieved from -\url{https://ggplot2.tidyverse.org} - -\leavevmode\vadjust pre{\hypertarget{ref-tidyverse}{}}% -Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., -François, R., Grolemund, G., et al. (2019). Welcome to the {tidyverse}. -\emph{Journal of Open Source Software}, \emph{4}(43), 1686. -doi:\href{https://doi.org/10.21105/joss.01686}{10.21105/joss.01686} - -\end{CSLReferences} - -\end{document}