kruskal.test
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. Non-numeric elements of a list will be coerced, with a warning. |
g | a vector or factor object giving the group for the corresponding elements of |
formula | a formula of the form |
data | an optional matrix or data frame (or similar: see |
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 |
... | 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 |
data.name | a character string giving the names of the data. |
References
Myles Hollander and Douglas A. Wolfe (1973), Nonparametric Statistical Methods. 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.
wilcox_test
in package coin for exact, asymptotic and Monte Carlo conditional p-values, including in the presence of ties.
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. require(graphics) boxplot(Ozone ~ Month, data = airquality) kruskal.test(Ozone ~ Month, data = airquality)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.