Performs a Kruskal-Wallis rank sum test.
kruskal.test(x, ...) ## S3 method for class 'default': kruskal.test((x, g, ...)) ## S3 method for class 'formula': kruskal.test((formula, data, subset, na.action, ...))
- a numeric vector of data values, or a list of numeric data vectors.
- a vector or factor object giving the group for the corresponding elements of
x. Ignored if
xis a list.
- a formula of the form
lhs ~ rhswhere
lhsgives the data values and
rhsthe corresponding groups.
- 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
- an optional vector specifying a subset of observations to be used.
- a function which indicates what should happen when the data contain
NAs. Defaults to
- 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.
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
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
A list with class
"htest" containing the following components:
- the Kruskal-Wallis rank sum statistic.
- the degrees of freedom of the approximate chi-squared distribution of the test statistic.
- the p-value of the test.
- the character string
"Kruskal-Wallis rank sum test".
- a character string giving the names of the data.
Myles Hollander and Douglas A. Wolfe (1973), Nonparametric Statistical Methods. 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.
wilcox_test in package coin for exact, asymptotic and Monte Carlo conditional p-values, including in the presence of ties.
## 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. require(graphics) boxplot(Ozone ~ Month, data = airquality) kruskal.test(Ozone ~ Month, data = airquality)
Documentation reproduced from R 3.0.2. License: GPL-2.