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Robust Breusch–Pagan test with bootstrap and effect sizes

Source: R/robust_implementations.R
performBPTestRobust.Rd

Enhances performBPTest() by optionally studentising residuals, resampling the test statistic via bootstrap, and reporting effect sizes together with power diagnostics.

Usage

performBPTestRobust(
  model,
  data,
  studentized = TRUE,
  bootstrap = FALSE,
  B = 1000,
  ci_level = 0.95,
  parallel = FALSE
)

Arguments

model

A fitted stats::lm object describing the mean structure whose residual variance is to be assessed.

data

A base::data.frame (or compatible object) containing the variables referenced in model. The data must include all observations used to fit model and should not contain unresolved missing values.

studentized

Logical, use studentized residuals for the auxiliary regression (Koenker variant)? Defaults to TRUE.

bootstrap

Logical, compute bootstrap diagnostics for the statistic and p-value?

B

Number of bootstrap replications when bootstrap = TRUE.

ci_level

Confidence level for reported intervals.

parallel

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

Value

An augmented htest object containing a robust_details element describing the additional diagnostics.

Details

Offers expanded reporting for the Breusch–Pagan family of tests including bootstrap p-values and asymptotic confidence intervals. The optional studentized argument toggles between the classic and Koenker versions.

References

Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294.

Koenker, R. (1981). A note on studentizing a test for heteroscedasticity. Journal of Econometrics, 17(1), 107–112.

See also

performBPTest() for the baseline test and performStudentizedBPTest() for the standalone studentised variant.

Examples

data(mtcars)
mod <- lm(mpg ~ wt + qsec, data = mtcars)
performBPTestRobust(mod, mtcars, bootstrap = TRUE, B = 200)
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#> 	Studentized Breusch-Pagan test (robust Studentized )
#> 
#> data:  model
#> X-squared = 3.0858, df = 2, p-value = 0.2138
#> alternative hypothesis: heteroscedasticity present
#>