Performs Anderson-Darling all-pairs comparison test.
adAllPairsTest(x, ...)# S3 method for default
adAllPairsTest(x, g, p.adjust.method = p.adjust.methods, ...)
# S3 method for formula
adAllPairsTest(
formula,
data,
subset,
na.action,
p.adjust.method = p.adjust.methods,
...
)
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.
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.
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").
For all-pairs comparisons in an one-factorial layout with non-normally distributed residuals Anderson-Darling's all-pairs comparison test can be used. A total of \(m = k(k-1)/2\) hypotheses can be tested. The null hypothesis H\(_{ij}: F_i(x) = F_j(x)\) is tested in the two-tailed test against the alternative A\(_{ij}: F_i(x) \ne F_j(x), ~~ i \ne j\).
This function is a wrapper function that sequentially
calls adKSampleTest for each pair.
The calculated p-values for Pr(>|T2N|)
can be adjusted to account for Type I error multiplicity
using any method as implemented in p.adjust.
Scholz, F.W., Stephens, M.A. (1987) K-Sample Anderson-Darling Tests. Journal of the American Statistical Association 82, 918--924.
adKSampleTest, adManyOneTest,
ad.pval.
adKSampleTest(count ~ spray, InsectSprays)
out <- adAllPairsTest(count ~ spray, InsectSprays, p.adjust="holm")
summary(out)
summaryGroup(out)
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