Performs pairwise comparisons of multiple group levels with one control.
manyOneUTest(x, ...)# S3 method for default
manyOneUTest(x, g, alternative = c("two.sided", "greater",
"less"), p.adjust.method = c("single-step", p.adjust.methods), ...)
# S3 method for formula
manyOneUTest(formula, data, subset, na.action,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = c("single-step", p.adjust.methods), ...)
a numeric vector of data values, or a list of numeric data vectors.
further arguments to be passed to or from methods.
a vector or factor object giving the group for the
corresponding elements of "x".
Ignored with a warning if "x" is a list.
the alternative hypothesis. Defaults to two.sided.
method for adjusting p values
(see p.adjust)
a formula of the form response ~ group where
response gives the data values and group a vector or
factor of the corresponding groups.
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).
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NAs. Defaults to getOption("na.action").
A list with class "PMCMR" containing the following components:
a character string indicating what type of test was performed.
a character string giving the name(s) of the data.
lower-triangle matrix of the estimated quantiles of the pairwise test statistics.
lower-triangle matrix of the p-values for the pairwise tests.
a character string describing the alternative hypothesis.
a character string describing the method for p-value adjustment.
a data frame of the input data.
a string that denotes the test distribution.
This functions performs Wilcoxon, Mann and Whitney's U-test for a one factorial design where each factor level is tested against one control (\(m = k -1\) tests). As the data are re-ranked for each comparison, this test is only suitable for balanced (or almost balanced) experimental designs.
For the two-tailed test and p.adjust.method = "single-step"
the multivariate normal distribution is used for controlling
Type 1 error and to calculate p-values. Otherwise,
the p-values are calculated from the standard normal distribution
with any latter p-adjustment as available by p.adjust.
OECD (ed. 2006) Current approaches in the statistical analysis of ecotoxicity data: A guidance to application, OECD Series on testing and assessment, No. 54.