kruskal.test {stats} | R Documentation |

Performs a Kruskal-Wallis rank sum test.

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

`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. |

`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`

.

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. |

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

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.

## 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 2.1.0 Index]