R: Pairwise Test for Multiple Comparisons of Mean Rank Sums with...

dunn.test.control {PMCMR}

R Documentation

Pairwise Test for Multiple Comparisons of Mean Rank Sums with one control (Dunn's-Test)

Description

Calculate pairwise multiple comparisons with one control according to Dunn.

Usage

dunn.test.control (x, g, p.adjust.method = p.adjust.methods, ...)

Arguments

`x`	a numeric vector of data values, or a list of numeric data vectors.
`g`	a vector or factor object giving the group for the corresponding elements of `x`. Ignored if `x` is a list.
`p.adjust.method`	Method for adjusting p values (see `p.adjust`).
`...`	further arguments to be passed to or from methods.

Details

For one-factorial designs with samples that do not meet the assumptions for one-way-ANOVA and subsequent post-hoc tests, the Kruskal-Wallis-Test kruskal.test can be employed that is also referred to as the Kruskal–Wallis one-way analysis of variance by ranks. Provided that significant differences were detected by this global test, one may be interested in applying post-hoc tests according to Dunn for pairwise multiple comparisons with one control.

See the vignette for details.

Value

A list with class "PMCMR"

`method`	The applied method.
`data.name`	The name of the data.
`p.value`	The two-sided p-value of the standard normal distribution.
`statistic`	The estimated quantile of the standard normal distribution.
`p.adjust.method`	The applied method for p-value adjustment.

Note

A tie correction will be employed according to Glantz (2012). As it is the case for multiple testing with one control using aov, the user must make sure that the control appears as the first level in the group vector. There is no formula method enclosed.

Author(s)

Thorsten Pohlert

References

O.J. Dunn (1964). Multiple comparisons using rank sums. Technometrics, 6, 241-252.

S. A. Glantz (2012), Primer of Biostatistics. New York: McGraw Hill.

S. Siegel, N. J. Castellan Jr. (1988), Nonparametric Statistics for The Behavioral Sciences. New York: McGraw-Hill.

Examples

##
require(stats) 
data(PlantGrowth)
attach(PlantGrowth)
kruskal.test(weight, group)
dunn.test.control(weight,group, "bonferroni")
detach(PlantGrowth)
rm(PlantGrowth)

[Package PMCMR version 4.3 Index]