kwsamplesize: Sample Size for the Kruskal-Wallis test.

In MultNonParam: Multivariate Nonparametric Methods

Sample Size for the Kruskal-Wallis test.

Description

kwsamplesize approximates sample size 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

kwsamplesize(
  shifts,
  distname = c("normal", "logistic", "cauchy"),
  targetpower = 0.8,
  proportions = rep(1, length(shifts))/length(shifts),
  level = 0.05,
  taylor = FALSE
)

Arguments

shifts	The offsets for the various populations, under the alternative hypothesis.
distname	The distribution of the underlying observations; normal and logistic are currently supported.
targetpower	The distribution of the underlying observations; normal and logistic are currently supported.
proportions	The proportions in each group.
level	The test level.
taylor	Logical flag forcing the approximation of exceedence probabilities using the first derivative at zero.

Details

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

Value

A list with the total number of observations needed to obtain approximate power, as long as this number is split amomg groups according to argument proportion.

Examples

#Calculate the sample size necessary to detect differences among three
#groups with centers at 0,1,2, from normal observations, using a test of
#level 0.05 and power 0.80.
kwsamplesize(c(0,1,2),"normal")

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