kruskal.test {stats} | R Documentation

## Kruskal-Wallis Rank Sum Test

### Description

Performs a Kruskal-Wallis rank sum test.

### Usage

    kruskal.test(x, ...)

    ## Default S3 method:
    kruskal.test(x, g, ...)

    ## S3 method for class 'formula':
    kruskal.test(formula, data, subset, na.action, ...)

### 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 `lhs ~ rhs` where `lhs` gives the data values and `rhs` the corresponding groups.
`data` | an optional data frame containing the variables in the model 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.

### Details

`kruskal.test` performs a Kruskal-Wallis rank sum test of the null that the location parameters of the distribution of `x` are the same in each group (sample). The alternative is that they differ in at least one.

If `x` is a list, its elements are taken as the samples to be compared, and hence have to be numeric data vectors. In this case, `g` is ignored, and one can simply use `kruskal.test(x)` to perform the test. If the samples are not yet contained in a list, use `kruskal.test(list(x, ...))`.

Otherwise, `x` must be a numeric data vector, and `g` must be a vector or factor object of the same length as `x` giving the group for the corresponding elements of `x`.

### Value

A list with class `"htest"` containing the following components:

`statistic` | the Kruskal-Wallis rank sum statistic.
`parameter` | the degrees of freedom of the approximate chi-squared distribution of the test statistic.
`p.value` | the p-value of the test.
`method` | the character string `"Kruskal-Wallis rank sum test"`.
`data.name` | a character string giving the names of the data.

### References

Myles Hollander & Douglas A. Wolfe (1973), Nonparametric statistical inference. New York: John Wiley & Sons. Pages 115–120.

### See Also

The Wilcoxon rank sum test (`wilcox.test`) as the special case for two samples; `lm` together with `anova` for performing one-way location analysis under normality assumptions; with Student's t test (`t.test`) as the special case for two samples.

### Examples

    ## Hollander & Wolfe (1973), 116.
    ## Mucociliary efficiency from the rate of removal of dust in normal
    ##  subjects, subjects with obstructive airway disease, and subjects
    ##  with asbestosis.
    x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
    y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
    z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
    kruskal.test(list(x, y, z))
    ## Equivalently,
    x <- c(x, y, z)
    g <- factor(rep(1:3, c(5, 4, 5)),
                labels = c("Normal subjects",
                           "Subjects with obstructive airway disease",
                           "Subjects with asbestosis"))
    kruskal.test(x, g)

    ## Formula interface.
    boxplot(Ozone ~ Month, data = airquality)
    kruskal.test(Ozone ~ Month, data = airquality)

[Package stats version 2.2.1 Index]