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

posthoc.kruskal.nemenyi.test {PMCMR}

R Documentation

Pairwise Test for Multiple Comparisons of Mean Rank Sums (Nemenyi-Tests)

Description

Calculate pairwise multiple comparisons between group levels. These tests are sometimes referred to as Nemenyi-tests for multiple comparisons of (mean) rank sums of independent samples.

Usage

posthoc.kruskal.nemenyi.test(x, ...)

## Default S3 method:
posthoc.kruskal.nemenyi.test( x, g, dist =
c("Tukey", "Chisquare"), ...)

## S3 method for class 'formula'
posthoc.kruskal.nemenyi.test(formula, data, subset,
na.action, dist =
c("Tukey", "Chisquare"), ...)

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 `NA`s. Defaults to `getOption("na.action")`.
`...`	further arguments to be passed to or from methods.
`dist`	the method for determining the p-value. The default distribution is `"Tukey"`, else `"Chisq"`.

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 Nemenyi for pairwise multiple comparisons of the ranked data.

See vignette("PMCMR") for details.

Value

A list with class "PMCMR"

`method`	The applied method.
`data.name`	The name of the data.
`p.value`	The p-value according to the studentized range distribution.
`statistic`	The estimated upper quantile of the studentized range distribution. (or quantile of Chisq distribution)
`p.adjust.method`	Defaults to "none"

Note

Only for method = "Chisq" a tie correction is employed.

Author(s)

Thorsten Pohlert

References

Lothar Sachs (1997), Angewandte Statistik. Berlin: Springer. Pages: 395-397, 662-664.

Examples

##
require(stats) 
data(InsectSprays)
attach(InsectSprays)
kruskal.test(count, spray)
posthoc.kruskal.nemenyi.test(count, spray)
posthoc.kruskal.nemenyi.test(count, spray, "Chisq")
detach(InsectSprays)
rm(InsectSprays)
## Formula Interface
posthoc.kruskal.nemenyi.test(count ~ spray, data = InsectSprays, dist="Tukey")

[Package PMCMR version 4.3 Index]