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.

### See Also

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

### Examples

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

[Package PMCMR version 4.2 Index]