Package {gglm}

Type: Package
Title: Grammar of Graphics for Linear Model Diagnostic Plots
Version: 1.1.0
Description: Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics. Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.
License: CC0
Date: 2026-08-01
Encoding: UTF-8
Depends: ggplot2
Imports: broom, broom.mixed, patchwork, utils, rlang, vctrs
Suggests: bslib, DT, fivethirtyeight, ggthemes, htmltools, lme4, palmerpenguins, RColorBrewer, shiny, testthat (≥ 3.0.0)
URL: https://github.com/graysonwhite/gglm, https://graysonwhite.com/gglm/
BugReports: https://github.com/graysonwhite/gglm/issues
RoxygenNote: 7.3.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-08-01 18:04:52 UTC; grwhite
Author: Grayson White ORCID iD [aut, cre]
Maintainer: Grayson White <graysonwhite13@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-01 18:20:02 UTC

gglm

Description

Provides four standard visual model diagnostic plots with ggplot2.

Usage

gglm(data, mapping = ggplot2::aes(), theme = ggplot2::theme_gray(), ...)

Arguments

data

A model object.

mapping

Additional aesthetic mappings to add to each layer, e.g. aes(color = some_var).

theme

The theme of the ggplots to be produced.

...

Additional arguments passed on to each stat_*() layer.

Value

A ggplot2 object for visual diagnostic of model validity.

Examples

data(mtcars)
m1 <- lm(mpg ~ cyl + disp + hp, data = mtcars)
gglm(m1)

launch

Description

Launches a R Shiny-based applet for linear model diagnostics.

Usage

launch(
  data = NULL,
  example_datasets = list(penguins = palmerpenguins::penguins, candy =
    fivethirtyeight::candy_rankings, mtcars = datasets::mtcars),
  ...
)

Arguments

data

A data.frame to use in modeling. Meant for one-off uses of launch() with data in your working environment.

example_datasets

A named list of example datasets to use in modeling. Meant for publicly hosting instances of launch() with a set of preferred datasets.

...

Currently ignored. For extendability.

Value

None. Launches a shiny applet for point-and-click based model diagnostics.

Examples

## Not run: 
launch()

## End(Not run)

list_model_classes

Description

Returns the possible model classes that gglm works with.

Usage

list_model_classes(...)

Arguments

...

Currently ignored. For extendability.

Value

A character vector containing the possible model classes that gglm works with.

Note

Note that these are not always the exact name of the class that that can be used. This is due to how some methods are written in the packages gglm imports. For example, the model class "merMod" refers to a variety of model outputs from lme4, even when the outputted class is not called "merMod".

Examples

list_model_classes()

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

broom

augment

stat_cooks_leverage

Description

Cook's Distance vs. Leverage

Usage

stat_cooks_leverage(
  mapping = ggplot2::aes(),
  alpha = 0.5,
  method = "loess",
  linecolor = "steelblue",
  se = FALSE,
  ...
)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(color = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

alpha

Adjust transparency of points.

method

Method for fitting the line to the points.

linecolor

Color of the line.

se

Keep standard error bands around line?

...

Additional arguments passed on to geom_point().

Value

A ggplot2 layer for plotting Cook's Distance vs. Leverage.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_cooks_leverage()

stat_cooks_obs

Description

ggplot2 layer for plotting cook's distance by observation number.

Usage

stat_cooks_obs(mapping = ggplot2::aes(), ...)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(fill = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

...

Additional arguments passed on to geom_col().

Value

A ggplot2 layer for plotting cook's distance by observation number.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_cooks_obs()

stat_fitted_resid

Description

ggplot2 layer for plotting a fitted vs. residual scatter plot.

Usage

stat_fitted_resid(mapping = ggplot2::aes(), alpha = 0.5, ...)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(color = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

alpha

Adjust transparency of points.

...

Additional arguments passed on to geom_point().

Value

A ggplot2 layer for plotting a fitted vs. residual scatter plot.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_fitted_resid()

stat_normal_qq

Description

Normal QQ plot.

Usage

stat_normal_qq(mapping = ggplot2::aes(), alpha = 0.5, ...)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(color = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

alpha

Adjust transparency of points.

...

Additional arguments passed on to geom_point().

Value

A ggplot2 layer for plotting a Normal Q-Q plot.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_normal_qq()

stat_resid_hist

Description

Visualize the distribution of the residuals of a model.

Usage

stat_resid_hist(mapping = ggplot2::aes(), bins = 30, ...)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(fill = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

bins

Adjust the number of bins.

...

Additional arguments passed on to geom_histogram().

Value

A ggplot2 layer for plotting a histogram of residuals.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_resid_hist()

stat_resid_leverage

Description

Residual vs. leverage plot.

Usage

stat_resid_leverage(
  mapping = ggplot2::aes(),
  alpha = 0.5,
  method = "loess",
  se = FALSE,
  linecolor = "steelblue",
  ...
)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(color = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

alpha

Adjust transparency of points.

method

Method for fitting the line to the points.

se

Keep standard error bands around line?

linecolor

Color of the line.

...

Additional arguments passed on to geom_point().

Value

A ggplot2 layer for plotting a fitted vs. residual scatter plot.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_resid_leverage()

stat_scale_location

Description

Scale location diagnostic plot.

Usage

stat_scale_location(
  mapping = ggplot2::aes(),
  alpha = 0.5,
  na.rm = TRUE,
  se = FALSE,
  method = "loess",
  linecolor = "steelblue",
  ...
)

Arguments

mapping

Additional aesthetic mappings to add to the layer, e.g. aes(color = some_var). If mapping includes a required aesthetic (e.g. x, y), the supplied value is ignored in favor of the default and a warning is issued.

alpha

Adjust the transparency of points.

na.rm

Remove points with value NA?

se

Keep standard error bands around line?

method

Method for fitting the line to the points.

linecolor

Color of the line.

...

Additional arguments passed on to geom_point().

Value

A ggplot2 layer for plotting the scale location diagnostic plot.

Examples

data(mtcars)
model <- lm(mpg ~ cyl + disp + hp, data = mtcars)
ggplot2::ggplot(data = model) + stat_scale_location()