kwpower: Power for the Kruskal-Wallis test.

In MultNonParam: Multivariate Nonparametric Methods

Power for the Kruskal-Wallis test.

Description

kwpower approximates power for the Kruskal-Wallis test, using a chi-square approximation under the null, and a non-central chi-square approximation under the alternative. The noncentrality parameter is calculated using alternative means and the null variance structure.

Usage

kwpower(
  nreps,
  shifts,
  distname = c("normal", "cauchy", "logistic"),
  level = 0.05,
  mc = 0,
  taylor = FALSE
)

Arguments

nreps	The numbers in each group.
shifts	The offsets for the various populations, under the alternative hypothesis.
distname	The distribution of the underlying observations; normal, cauchy, and logistic are currently supported.
level	The test level.
mc	0 for asymptotic calculation, or positive for mc approximation.
taylor	logical determining whether Taylor series approximation is used for probabilities.

Details

The standard noncentral chi-square power formula, or Monte Carlo, is used.

Value

A list with components power, giving the power approximation, ncp, giving the noncentrality parameter, cv, giving the critical value, probs, giving the intermediate output from pairwiseprobability, and expect, the quantities summed before squaring in the noncentrality parameter.

Examples

#Calculate the power for the Kruskal Wallis test for normal observations,
#10 observations in each of three groups, with groups centered at 0, 1, 2.
#Level is 0.05 by default.
kwpower(rep(10,3),c(0,1,2),"normal")

MultNonParam documentation built on Aug. 30, 2023, 9:09 a.m.