# posthoc.kruskal.nemenyi.test: Pairwise Test for Multiple Comparisons of Mean Rank Sums... In PMCMR: Calculate Pairwise Multiple Comparisons of Mean Rank Sums

## 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

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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.

Thorsten Pohlert

## References

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

`kruskal.test`, `friedman.test`, `posthoc.friedman.nemenyi.test`, `Tukey`, `Chisquare`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## 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") ```