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Plot Residuals vs. Fitted Values to Assess Homoskedasticity

Source: R/lm_plot.var.R
lm_plot.var.Rd

Produces a scatter plot of residuals against fitted values from a linear model, highlighting outlier points and optionally displaying the Breusch-Pagan test p-value for heteroskedasticity.

Usage

lm_plot.var(
  mdl,
  opt = list(),
  parm = list(),
  df = lm_plot.df(mdl),
  plts = list()
)

Arguments

mdl

A fitted model object (typically from lm).

opt

List of options, where pval.BP (logical, default = FALSE) indicates whether to include Breusch-Pagan p-value on the plot.

parm

List of plotting parameters, usually from lm_plot.parms().

df

Data frame with augmented model data. Defaults to lm_plot.df(mdl).

plts

List of ggplot objects to which this plot will be added.

Value

A list containing:

  • mdl Fitted model object,

  • opt Options used, including pval.BP,

  • parm Parameter list with Breusch-Pagan test results added,

  • df Data frame used for plotting,

  • plts List of ggplot objects, including the $var element.

Details

The plot visualizes residuals versus fitted values to assess homoskedasticity (constant variance). Points are colored and shaped by outlier status, and outlier/regular points can be labeled. If enabled, the Breusch-Pagan test for heteroskedasticity is run and its p-value is annotated on the plot.

See also

lm_plot.df, lm_plot.parms, bptest

Examples

mdl <- lm(Sepal.Length ~ Sepal.Width, data = iris)
result <- lm_plot.var(mdl)
print(result$plts$var)