PMCMRplus (version 1.6.0)

flignerWolfeTest: Testing Several Treatments With One Control

Description

Performs Fligner-Wolfe non-parametric test for simultaneous testing of several locations (treatment groups) against the location of the control group.

Usage

flignerWolfeTest(x, ...)
# S3 method for default
flignerWolfeTest(
  x,
  g,
  alternative = c("greater", "less"),
  dist = c("Wilcoxon", "Normal"),
  ...
)
# S3 method for formula
flignerWolfeTest(
  formula,
  data,
  subset,
  na.action,
  alternative = c("greater", "less"),
  dist = c("Wilcoxon", "Normal"),
  ...
)

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.

alternative

the alternative hypothesis. Defaults to "greater".

dist

the test distribution. Defaults to "Wilcoxon".

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").

Value

A list with class "htest" containing the following components:

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

statistic

the estimated quantile of the test statistic.

p.value

the p-value for the test.

parameter

the parameters of the test statistic, if any.

alternative

a character string describing the alternative hypothesis.

estimates

the estimates, if any.

null.value

the estimate under the null hypothesis, if any.

Details

Let there be \(k\)-treatment groups and one control group, then the null hypothesis, H\(_0: \theta_i - \theta_c = 0 ~ (1 \le i \le k)\) is tested against the alternative (greater), H\(_\mathrm{A}: \theta_i - \theta_c > 0 ~ (1 \le i \le k)\), with at least one inequality being strict.

If dist = Wilcoxon, then the p-values are estimated from the Wilcoxon distribution, else the Normal distribution is used. The latter can be used, if ties are present.

References

Fligner, M.A., Wolfe, D.A. (1982) Distribution-free tests for comparing several treatments with a control. Stat Neerl 36, 119--127.

See Also

kruskalTest and shirleyWilliamsTest of the package PMCMRplus, kruskal.test of the library stats.

Examples

# NOT RUN {
## Example from Sachs (1997, p. 402)
x <- c(106, 114, 116, 127, 145,
       110, 125, 143, 148, 151,
       136, 139, 149, 160, 174)
g <- gl(3,5)
levels(g) <- c("A", "B", "C")

## Chacko's test
chackoTest(x, g)

## Cuzick's test
cuzickTest(x, g)

## Johnson-Mehrotra test
johnsonTest(x, g)

## Jonckheere-Terpstra test
jonckheereTest(x, g)

## Le's test
leTest(x, g)

## Spearman type test
spearmanTest(x, g)

## Murakami's BWS trend test
bwsTrendTest(x, g)

## Fligner-Wolfe test
flignerWolfeTest(x, g)

# }