kruskal.test
Kruskal-Wallis Rank Sum Test
Performs a Kruskal-Wallis rank sum test.
- Keywords
- htest
Usage
kruskal.test(x, ...)
"kruskal.test"(x, g, ...)
"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
x. Ignored with a warning ifxis a list. - formula
- a formula of the form
response ~ groupwhereresponsegives the data values andgroupa vector or factor of the corresponding groups. - data
- an optional matrix or data frame (or similar: see
model.frame) containing the variables in the formulaformula. By default the variables are taken fromenvironment(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
NAs. Defaults togetOption("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
- 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.
"htest" containing the following components:
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
\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}coincoin for exact, asymptotic and Monte Carlo
conditional p-values, including in the presence of ties.
Examples
library(stats)
## 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)