View source: R/adManyOneTest.R
adManyOneTest | R Documentation |
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 \le i \le 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.
Factor labels for g
must be assigned in such a way,
that they can be increasingly ordered from zero-dose
control to the highest dose level, e.g. integers
{0, 1, 2, ..., k} or letters {a, b, c, ...}.
Otherwise the function may not select the correct values
for intended zero-dose control.
It is safer, to i) label the factor levels as given above,
and to ii) sort the data according to increasing dose-levels
prior to call the function (see order
, factor
).
Scholz, F.W., Stephens, M.A. (1987) K-Sample Anderson-Darling Tests. Journal of the American Statistical Association 82, 918–924.
adKSampleTest
, adAllPairsTest
,
ad.pval
.
## Data set PlantGrowth
## Global test
adKSampleTest(weight ~ group, data = PlantGrowth)
##
ans <- adManyOneTest(weight ~ group,
data = PlantGrowth,
p.adjust.method = "holm")
summary(ans)
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