Anderson-Darling Many-To-One Comparison Test
Performs Anderson-Darling many-to-one comparison test.
adManyOneTest(x, ...) ## Default S3 method: adManyOneTest(x, g, p.adjust.method = p.adjust.methods, ...) ## S3 method for class 'formula' adManyOneTest( formula, data, subset, na.action, p.adjust.method = p.adjust.methods, ... )
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 |
p.adjust.method |
method for adjusting
p values (see |
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 |
For many-to-one comparisons (pairwise comparisons with one control) in an one-factorial layout with non-normally distributed residuals Anderson-Darling's non-parametric test can be performed. Let there be k groups including the control, then the number of treatment levels is m = k - 1. Then m pairwise comparisons can be performed between the i-th treatment level and the control. H_i: F_0 = F_i is tested in the two-tailed case against A_i: F_0 \ne F_i, ~~ (1 ≤ i ≤ m).
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 inflation
using any method as implemented in p.adjust.
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.
Scholz, F.W., Stephens, M.A. (1987) K-Sample Anderson-Darling Tests. Journal of the American Statistical Association 82, 918–924.
## Data set PlantGrowth
## Global test
adKSampleTest(weight ~ group, data = PlantGrowth)
##
ans <- adManyOneTest(weight ~ group,
data = PlantGrowth,
p.adjust.method = "holm")
summary(ans)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.