kruskal.test(y, groups)
NAs are allowed, but
will be removed.
Infs are allowed, and are not removed as they are
rankable.
y, giving the group (treatment) for
each corresponding element of
y.
NAs and
Infs are not allowed.
If
groups is not a factor or category object, it will be coerced to one.
"htest", containing the following components:
names
attribute
"Kruskal-Wallis chi-square". See section DETAILS for a definition.
statistic. Component
parameters has
names attribute
"df".
"two.sided", to reflect that the implicit alternative hypothesis is
two-sided.
y and
groups.
In the context of a one-way layout with factor
groups,
a typical null hypothesis is that the true location parameter for
y
is the same in each of the
groups.
The alternative hypothesis is that it is different in at least one of the
groups.
See Hollander and Wolfe (1973) for alternate models.
The elements of
y are assumed to consist of a
groups effect plus
independent and identically distributed residual errors.
The returned
p.value should be interpreted carefully. It is only a
large-sample approximation whose validity increases with the smallest of
the group sizes.
Hollander, M. and Wolfe, D. A. (1973).
Nonparametric Statistical Methods.
New York: John Wiley.
Lehmann, E. L. (1975).
Nonparametrics: Statistical Methods Based on Ranks.
Oakland, Calif.: Holden-Day.
# Data from Hollander and Wolfe (1973), p. 116
holl.y <- c(2.9,3.0,2.5,2.6,3.2,3.8,2.7,4.0,2.4,2.8,3.4,3.7,2.2,2.0)
holl.grps <- factor(c(1,1,1,1,1,2,2,2,2,3,3,3,3,3),
labels=c("Normal Subjects","Obstr. Airway Disease","Asbestosis"))
kruskal.test(holl.y, holl.grps)
# Now suppose the data is in the form of a table already,
# with groups in columns; note this implies that group
# sizes are the same.
tab.data <- matrix(c(.38,.58,.15,.72,.09,.66,.52,.02,.59,.94,
.24,.94,.08,.97,.47,.92,.59,.77), ncol=3)
tab.data
# Generate 'y' and 'groups':
y2 <- as.vector(tab.data)
gr <- factor(as.vector(col(tab.data))) # Groups are columns
kruskal.test(y2, gr)