Rank-based permutation heteroscedasticity test

Performs a non-parametric permutation test based on the rank correlation between absolute residuals and a chosen ordering variable. Significant correlations imply systematic changes in residual spread.

Usage

performRankPermutationTest(
  model,
  data,
  order_by = NULL,
  B = 999,
  progress = interactive()
)

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.

order_by

Optional name of a predictor variable used to rank the observations. Defaults to the first non-intercept term in the model matrix.

B

Number of permutation replications used to approximate the null distribution.

progress

Logical toggle for progress reporting during permutations.

Value

An htest object containing the observed Spearman correlation and a permutation-based p-value.

References

Hollander, M., Wolfe, D. A., & Chicken, E. (2013). Nonparametric Statistical Methods (3rd ed.). Wiley.

Examples

data(mtcars)
model <- lm(mpg ~ wt + hp, data = mtcars)
set.seed(42)
performRankPermutationTest(model, mtcars, B = 199, progress = FALSE)
#> 
#> 	Rank permutation heteroscedasticity test
#> 
#> data:  mpg ~ wt + hp
#> rho = -0.16173, B = 199, p-value = 0.39
#>