PMCMR (version 4.3)

# posthoc.kruskal.dunn.test: Pairwise Test for Multiple Comparisons of Mean Rank Sums (Dunn's-Test)

## Description

Calculate pairwise multiple comparisons between group levels according to Dunn.

## Usage

posthoc.kruskal.dunn.test(x, …)

# S3 method for default posthoc.kruskal.dunn.test( x, g, p.adjust.method = p.adjust.methods, …)

# S3 method for formula posthoc.kruskal.dunn.test(formula, data, subset, na.action, 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.

formula

a formula of the form response ~ group where response gives the data values and group a vector or factor of the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

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.

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 of the ranked data.

See vignette("PMCMR") 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.

## Examples

Run this code
# NOT RUN {
##
require(stats)
data(InsectSprays)
attach(InsectSprays)
kruskal.test(count, spray)
posthoc.kruskal.dunn.test(count, spray, "bonferroni")
detach(InsectSprays)
rm(InsectSprays)
## Formula Interface
posthoc.kruskal.dunn.test(count ~ spray, data = InsectSprays, p.adjust="bonf")
# }

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