# dunn.test.control: Pairwise Test for Multiple Comparisons of Mean Rank Sums with... In PMCMR: Calculate Pairwise Multiple Comparisons of Mean Rank Sums

## Description

Calculate pairwise multiple comparisons with one control according to Dunn.

## Usage

 `1` ```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.

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.

`kruskal.test`, `friedman.test`, `posthoc.friedman.nemenyi.test`, `pnorm`, `p.adjust`
 ```1 2 3 4 5 6 7 8``` ```## require(stats) data(PlantGrowth) attach(PlantGrowth) kruskal.test(weight, group) dunn.test.control(weight,group, "bonferroni") detach(PlantGrowth) rm(PlantGrowth) ```