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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,
  ...,
  pval.BP = FALSE,
  parms = lm_plot.parms(mdl),
  df = lm_plot.df(mdl, parms = parms)
)

Arguments

mdl

A fitted model object (typically from lm).

...

Additional arguments (not currently used).

pval.BP

(logical, default = FALSE) option to include Breusch-Pagan p-value on the plot.

parms

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

df

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

Value

A ggplot object representing the residuals versus fitted values plot. Included as an attribute "parms" is a list containing:

  • lim Plotted limits on x and y axes,

  • pval.BP Option to show Breusch-Pagan p-value,

  • BP The htest object with Breusch-Pagan test results.

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. The Breusch-Pagan test for heteroskedasticity is run and, if enabled, its p-value annotates the plot.

See also

lm_plot.df, lm_plot.parms, bptest

Examples

mdl <- lm(Sepal.Length ~ Sepal.Width, data = iris)
lm_plot.var(mdl, pval.BP = TRUE)