higgins.fisher.kruskal.test {MultNonParam}

Fisher's LSD method applied to the Kruskal-Wallis test

Description

This function applies a rank-based method for controlling experiment-wise error. Two hypothesis have to be respected: normality of the distribution and no ties in the data. The aim is to be able to detect, among k treatments, those who lead to significant differencies in the values for a variable of interest.

Usage

higgins.fisher.kruskal.test(resp, grp, alpha = 0.05)

Arguments

resp

vector containing the values for the variable of interest.

grp

vector specifying in which group is each observation.

alpha

level of the test.

Details

First, the Kruskal-Wallis test is used to test the equality of the distributions of each treatment. If the test is significant at the level alpha, the method can be applied.

Value

A matrix with two columns. Each row indicates a combinaison of two groups that have significant different distributions.

References

J.J. Higgins, (2004), Introduction to Modern Nonparametric Statistics, Brooks/Cole, Cengage Learning.