Panel of Interactive Versions of Diagnostic Residual Plots.

Creates a panel of interactive residual diagnostic plots given a model. Currently accepts models of type "lm", "glm", "lmerMod", "lmerModLmerTest", "lme", and "glmerMod".

Usage

resid_interact(
  model,
  plots = "default",
  type = NA,
  bins = 30,
  smoother = TRUE,
  qqline = TRUE,
  scale = 0.9,
  theme = "bw",
  axis.text.size = 10,
  title.text.size = 12,
  title.opt = TRUE,
  nrow = NULL,
  alpha = 0.6,
  coordfix = FALSE
)

Arguments

model: Model fit using either lm, glm, lmer, lmerTest, lme, or glmer.
plots: Plots chosen to include in the panel of plots. The default panel includes a residual plot, a normal quantile plot, an index plot, and a histogram of the residuals. (See details in the help file for resid_panel for the options available.)
type: Type of residuals to use in the plot. If not specified, the default residual type for each model type is used. (See details in the help file for resid_panel for the options available.)
bins: Number of bins to use when creating a histogram of the residuals. Default is set to 30.
smoother: Indicates whether or not to include a smoother on the residual vs fitted and index plots. Specify TRUE or FALSE. Default is set to TRUE.
qqline: Indicates whether to include a 1-1 line on the qq-plot. Specify TRUE or FALSE. Default is set to TRUE. (The option of qqbands has not been implemented in plotly, so it is not available as an option with resid_interact.)
scale: Scales the size of the graphs in the panel. Takes values in (0,1].
theme: ggplot2 theme to be used. Current options are "bw", "classic", and "grey" (or "gray"). Default is "bw".
axis.text.size: Specifies the size of the text for the axis labels of all plots in the panel.
title.text.size: Specifies the size of the text for the titles of all plots in the panel.
title.opt: Indicates whether or not to include a title on the plots in the panel. Specify TRUE or FALSE. Default is set to TRUE.
nrow: Sets the number of rows in the panel.
alpha: Sets the alpha level for displays with points. Default is set to 0.6.
coordfix: Uses fixed aspect ratio for QQ-plots and yvp. Default is FALSE.

Value

A panel of interactive residual diagnostic plots containing plots specified.

Details

Details on the creation of the plots can be found in the details section of the help file for resid_panel.

Examples


# Fit a model to the penguin data
penguin_model <- lme4::lmer(heartrate ~ depth + duration + (1|bird), data = penguins)

# Create the default interactive panel
resid_interact(penguin_model)
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?


# Select only the residual plot and qq-plot to be included in the panel,
# set the number of rows to 2, change the theme to classic
resid_interact(penguin_model, plots = c("resid", "qq"), nrow = 2, theme = "classic")
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?