higgins.fisher.kruskal.test

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.

A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

John E Kolassa

MultNonParam

Multivariate Nonparametric Methods

John E. Kolassa

Stephane Jankowski

higgins.fisher.kruskal.test function

<dl><dt>resp</dt>
<dd>vector containing the values for the variable of interest.</dd>
<dt>grp</dt>
<dd>vector specifying in which group is each observation.</dd>
<dt>alpha</dt>
<dd>level of the test.</dd></dl>

Arguments

Fisher's LSD method applied to the Kruskal-Wallis test — higgins.fisher.kruskal.test

<dl>

<dt>resp</dt>
<dd>vector containing the values for the variable of interest.</dd>


<dt>grp</dt>
<dd>vector specifying in which group is each observation.</dd>


<dt>alpha</dt>
<dd>level of the test.</dd>

</dl>

higgins.fisher.kruskal.test: Fisher's LSD method applied to the Kruskal-Wallis test

Description

Usage

Value

Arguments

Details

References