Run enhanced heteroscedasticity diagnostics

Executes one or more robust diagnostics with automatic enhancements for small samples and optional studentisation.

Usage

rrunAdvancedDiagnostics(
  model,
  data,
  tests = "all",
  auto_enhance = TRUE,
  bootstrap_B = 500,
  parallel = FALSE,
  ci_level = 0.95
)

Arguments

model

A fitted stats::lm object representing the mean specification to be diagnosed.

data

A base::data.frame (or object coercible to one) containing the variables referenced by model. It must include the observations used to fit model and will be checked for missing values.

tests

Character vector of tests to run. Use "all" (default) to run the White and Breusch-Pagan diagnostics, or supply a subset such as c("white", "bp").

auto_enhance

Logical, enable automatic bootstrap for small samples (n < 50) and studentization for Breusch-Pagan.

bootstrap_B

Number of bootstrap replications used when automatic enhancement triggers bootstrap.

parallel

Logical, allow parallel bootstrap evaluation when the parallel package is available.

ci_level

Confidence level for reported intervals.

Value

A list with the executed results and metadata describing the enhancements that were applied.