Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/graysonwhite/gglm

Grammar of Graphics for Linear Model Diagnostic Plots
https://github.com/graysonwhite/gglm

diagnostic-plots grammar-of-graphics linear-model-diagnostics r

Last synced: 3 months ago
JSON representation

Grammar of Graphics for Linear Model Diagnostic Plots

Awesome Lists containing this project

README 

        
---

output: github_document

---



```{r, include = F}

knitr::opts_chunk$set(

  fig.path = "man/figures/"

)

```



[![CRAN status](https://www.r-pkg.org/badges/version/gglm)](https://cran.r-project.org/package=gglm)

[![R-CMD-check](https://github.com/graysonwhite/gglm/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/graysonwhite/gglm/actions/workflows/R-CMD-check.yaml)



# gglm 



## Overview



`gglm`, The Grammar of Graphics for Linear Model Diagnostics, is an R package and [official `ggplot2` extension](https://exts.ggplot2.tidyverse.org/gallery/) that creates beautiful diagnostic plots using `ggplot2` for a variety of model objects. These diagnostic plots are easy to use and adhere to the Grammar of Graphics. The purpose of this package is to provide a sensible alternative to using the base-R `plot()` function to produce diagnostic plots for model objects. Currently, `gglm` supports all model objects that are supported by `broom::augment()`, `broom.mixed::augment()`, or `ggplot2::fortify()`. For example, objects that are outputted from `stats::lm()`, `lme4::lmer()`, `brms::brm()`, and many other common modeling functions will work with `gglm`. The function `gglm::list_model_classes()` provides a full list of model classes supported by `gglm`. 



## Installation 



`gglm` can be installed from CRAN:

```{r eval = FALSE}

install.packages("gglm")

```



Or, the developmental version of `gglm` can be installed from GitHub:

```{r eval = FALSE}

devtools::install_github("graysonwhite/gglm")

```



## Examples



`gglm` has two main types of functions. First, the `gglm()` function is used for quickly creating the four main diagnostic plots, and behaves similarly to how `plot()` works on an `lm` type object. Second, the `stat_*()` functions are used to produce diagnostic plots by creating `ggplot2` layers. These layers allow for plotting of particular model diagnostic plots within the `ggplot2` framework. 



### Example 1: Quickly creating the four diagnostic plots with `gglm()`



Consider a simple linear model used to predict miles per gallon with weight. We can fit this model with `lm()`, and then diagnose it easily by using `gglm()`.

```{r, message=F}

library(gglm) # Load gglm



model <- lm(mpg ~ wt, data = mtcars) # Fit the simple linear model



gglm(model) # Plot the four main diagnostic plots

```



Now, one may be interested in a more complicated model, such as a mixed model with a varying intercept on `cyl`, fit with the `lme4` package. Luckily, `gglm` accommodates a variety of models and modeling packages, so the diagnostic plots for the mixed model can be created in the same way as they were for the simple linear model. 

```{r, message=F}

library(lme4) # Load lme4 to fit the mixed model



mixed_model <- lmer(mpg ~ wt + (1 | cyl), data = mtcars) # Fit the mixed model



gglm(mixed_model) # Plot the four main diagnostic plots.

```



### Example 2: Using the Grammar of Graphics with the `stat_*()` functions



`gglm` also provides functionality to stay within the Grammar of Graphics by providing functions that can be used as `ggplot2` layers. An example of one of these functions is the `stat_fitted_resid()` function. With this function, we can take a closer look at just the fitted vs. residual plot from the mixed model fit in Example 1.



```{r}

ggplot(data = mixed_model) +

  stat_fitted_resid()

```



After taking a closer look, we may want to clean up the look of the plot for a presentation or a project. This can be done by adding other layers from `ggplot2` to the plot. Note that any `ggplot2` layers can be added on to any of the `stat_*()` functions provided by `gglm`. 



```{r}

ggplot(data = mixed_model) +

  stat_fitted_resid(alpha = 1) + 

  theme_bw() + # add a clean theme 

  labs(title = "Residual vs fitted values for the mixed model") + # change the title

  theme(plot.title = element_text(hjust = 0.5)) # center the title

```



Wow! What a beautiful and production-ready diagnostic plot!



## Functions



### For quick and easy plotting



`gglm()` plots the four default diagnostic plots when supplied a model object (this is similar to `plot.lm()` in the case of an object generated by `lm()`). Note that can `gglm()` take many types of model object classes as its input, and possible model object classes can be seen with `list_model_classes()`



### Following the Grammar of Graphics



`stat_normal_qq()`, `stat_fitted_resid()`, `stat_resid_hist()`, `stat_scale_location()`, `stat_cooks_leverage()`, `stat_cooks_obs()`, and `stat_resid_leverage()` all are `ggplot2` layers used to create individual diagnostic plots. To use these, follow Example 2. 



### Other functions



`list_model_classes()` lists the model classes compatible with `gglm`.