## Overview

`gglm`

, The Grammar of Graphics for Linear Model Diagnostics, is an R package and official `ggplot2`

extension 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:

`install.packages("gglm")`

Or, the developmental version of `gglm`

can be installed from GitHub:

`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()`

.

```
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.

### 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.

```
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`

.

```
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`

.