medianTest {PMCMRplus} | R Documentation |
Brown-Mood Median Test
Description
Performs Brown-Mood Median Test.
Usage
medianTest(x, ...)
## Default S3 method:
medianTest(x, g, simulate.p.value = FALSE, B = 2000, ...)
## S3 method for class 'formula'
medianTest(
formula,
data,
subset,
na.action,
simulate.p.value = FALSE,
B = 2000,
...
)
Arguments
x |
a numeric vector of data values, or a list of numeric data vectors. |
... |
further arguments to be passed to or from methods. |
g |
a vector or factor object giving the group for the
corresponding elements of |
simulate.p.value |
a logical indicating whether to compute p-values by Monte-Carlo simulation. |
B |
an integer specifying the number of replicates used in the Monte-Carlo test. |
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 |
Details
The null hypothesis, H_0: \theta_1 = \theta_2 =
\ldots = \theta_k
is tested against the alternative,
H_\mathrm{A}: \theta_i \ne \theta_j ~~(i \ne j)
, with at least
one unequality beeing strict.
Value
A list with class ‘htest’. For details see
chisq.test
.
References
Brown, G.W., Mood, A.M., 1951, On Median Tests for Linear Hypotheses, in: Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, pp. 159–167.
See Also
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
g <- factor(x = c(rep(1, length(x)),
rep(2, length(y)),
rep(3, length(z))),
labels = c("ns", "oad", "a"))
dat <- data.frame(
g = g,
x = c(x, y, z))
## AD-Test
adKSampleTest(x ~ g, data = dat)
## BWS-Test
bwsKSampleTest(x ~ g, data = dat)
## Kruskal-Test
## Using incomplete beta approximation
kruskalTest(x ~ g, dat, dist="KruskalWallis")
## Using chisquare distribution
kruskalTest(x ~ g, dat, dist="Chisquare")
## Not run:
## Check with kruskal.test from R stats
kruskal.test(x ~ g, dat)
## End(Not run)
## Using Conover's F
kruskalTest(x ~ g, dat, dist="FDist")
## Not run:
## Check with aov on ranks
anova(aov(rank(x) ~ g, dat))
## Check with oneway.test
oneway.test(rank(x) ~ g, dat, var.equal = TRUE)
## End(Not run)
## Median Test asymptotic
medianTest(x ~ g, dat)
## Median Test with simulated p-values
set.seed(112)
medianTest(x ~ g, dat, simulate.p.value = TRUE)