PMCMR (version 4.3)

dunn.test.control: 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.

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.

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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<U+2013>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.

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.

See Also

kruskal.test, friedman.test, posthoc.friedman.nemenyi.test, pnorm, p.adjust

Examples

# NOT RUN {
##
require(stats) 
data(PlantGrowth)
attach(PlantGrowth)
kruskal.test(weight, group)
dunn.test.control(weight,group, "bonferroni")
detach(PlantGrowth)
rm(PlantGrowth)
# }