Type:	Package
Title:	Grammar of Graphics for Linear Model Diagnostic Plots
Version:	1.0.4
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:	2025-04-01
Encoding:	UTF-8
Depends:	ggplot2
Imports:	broom, broom.mixed, cli, dplyr, metafor, nlme, patchwork, purrr, tibble, tidyr, utils, rlang, vctrs
Suggests:	lme4, testthat (≥ 3.0.0)
URL:	https://github.com/graysonwhite/gglm, https://graysonwhite.com/gglm/
BugReports:	https://github.com/graysonwhite/gglm/issues
RoxygenNote:	7.2.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-04-01 19:27:44 UTC; graysonwhite
Author:	Grayson White [aut, cre]
Maintainer:	Grayson White <graysonwhite13@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-01 19:40:02 UTC

gglm

Description

Provides four standard visual model diagnostic plots with 'ggplot2'.

Usage

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

Arguments

Value

A a 'ggplot2' object for visual diagnostic of model validity.

Examples

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

list_model_classes

Description

Returns the possible model classes that 'gglm' works with.

Usage

list_model_classes(...)

Arguments

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(
  alpha = 0.5,
  method = "loess",
  color = "steelblue",
  se = FALSE,
  ...
)

Arguments

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(...)

Arguments

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(alpha = 0.5, ...)

Arguments

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(alpha = 0.5, ...)

Arguments

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(bins = 30, ...)

Arguments

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(
  alpha = 0.5,
  method = "loess",
  se = FALSE,
  color = "steelblue",
  ...
)

Arguments

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(
  alpha = 0.5,
  na.rm = TRUE,
  se = FALSE,
  method = "loess",
  color = "steelblue",
  ...
)

Arguments

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()