Rank-based permutation heteroscedasticity test

Source: R/modern_diagnostics.R · Help: ?performRankPermutationTest

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 = NULL,
  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

The data frame model was fitted to, or NULL, the default. The response, design, weights and offset are read from the fit itself, so a model that transforms its variables, has an offset() term, or takes its weights or offset from a column of the data needs no data, and the model frame of model is accepted as data (since 0.16.0; before, such models needed data), unless order_by names a variable the model frame does not hold, which is then read from data. Data that are given are matched to the observations of the fit: rows with row names of their own by name, in any order; rows that only number themselves (a tibble, a data.table, a data frame whose row names were reset) by the row numbers of the fit, or in the order of the fit when they hold one row per observation and, when order_by is read from data, no two rows of the fit tie on every column of its model frame. The rows the fit dropped may be there or not. Data whose rows cannot be shown to hold the observations of the fit are refused; see test_results.

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. Defaults to 999, the default of every resampling test in the package.

progress

Logical toggle for progress reporting during permutations.

Value

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

Supported models

It accepts an lm and a glm with the Gaussian family and the identity link, unweighted or with positive weights, a fitWLS() fit and, since 0.16.0, a rank-deficient fit, which it tests on the estimable part of its design: it gives the value of the same model fitted without the aliased columns. It refuses a glm of another family or link, a survey::svyglm() fit, a fit with a zero weight, a coefficient that is not finite for another reason, and data that lack rows or variables of the fit. Which model each test accepts, and why, is set out in test_results, under Supported models.

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)