PMCMRplus (version 1.9.12)

medianTest: Brown-Mood Median Test

Description

Performs Brown-Mood Median Test.

Usage

medianTest(x, ...)
# S3 method for default
medianTest(x, g, simulate.p.value = FALSE, B = 2000, ...)
# S3 method for formula
medianTest(
  formula,
  data,
  subset,
  na.action,
  simulate.p.value = FALSE,
  B = 2000,
  ...
)

Value

A list with class ‘htest’. For details see chisq.test.

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 "x". Ignored with a warning if "x" is a list.

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 response ~ group where response gives the data values and group a vector or factor of the corresponding groups.

data

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 environment(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 to getOption("na.action").

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.

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

chisq.test.

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")

if (FALSE) {
## Check with kruskal.test from R stats
kruskal.test(x ~ g, dat)
}
## Using Conover's F
kruskalTest(x ~ g, dat, dist="FDist")

if (FALSE) {
## Check with aov on ranks
anova(aov(rank(x) ~ g, dat))
## Check with oneway.test
oneway.test(rank(x) ~ g, dat, var.equal = TRUE)
}

## Median Test asymptotic
medianTest(x ~ g, dat)

## Median Test with simulated p-values
set.seed(112)
medianTest(x ~ g, dat, simulate.p.value = TRUE)