Nemenyi-Damico-Wolfe Many-to-One Rank Comparison Test
Performs Nemenyi-Damico-Wolfe non-parametric many-to-one comparison test for Kruskal-type ranked data.
kwManyOneNdwTest(x, ...)
## Default S3 method:
kwManyOneNdwTest(
x,
g,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = c("single-step", p.adjust.methods),
...
)
## S3 method for class 'formula'
kwManyOneNdwTest(
formula,
data,
subset,
na.action,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = c("single-step", 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 |
alternative |
the alternative hypothesis. Defaults to |
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 the Nemenyi-Damico-Wolfe 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: θ_0 = θ_i is tested in the two-tailed case against A_i: θ_0 \ne θ_i, ~~ (1 ≤ i ≤ m).
If p.adjust.method == "single-step" is selected,
the p-values will be computed
from the multivariate normal distribution. Otherwise,
the p-values are computed from the standard normal distribution using
any of the p-adjustment methods as included 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.
This function is essentially the same as kwManyOneDunnTest, but
there is no tie correction included. Therefore, the implementation of
Dunn's test is superior, when ties are present.
Damico, J. A., Wolfe, D. A. (1989) Extended tables of the exact distribution of a rank statistic for treatments versus control multiple comparisons in one-way layout designs, Communications in Statistics - Theory and Methods 18, 3327–3353.
Nemenyi, P. (1963) Distribution-free Multiple Comparisons, Ph.D. thesis, Princeton University.
## Data set PlantGrowth
## Global test
kruskalTest(weight ~ group, data = PlantGrowth)
## Conover's many-one comparison test
## single-step means p-value from multivariate t distribution
ans <- kwManyOneConoverTest(weight ~ group, data = PlantGrowth,
p.adjust.method = "single-step")
summary(ans)
## Conover's many-one comparison test
ans <- kwManyOneConoverTest(weight ~ group, data = PlantGrowth,
p.adjust.method = "holm")
summary(ans)
## Dunn's many-one comparison test
ans <- kwManyOneDunnTest(weight ~ group, data = PlantGrowth,
p.adjust.method = "holm")
summary(ans)
## Nemenyi's many-one comparison test
ans <- kwManyOneNdwTest(weight ~ group, data = PlantGrowth,
p.adjust.method = "holm")
summary(ans)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.