Conover's Many-to-One Rank Comparison Test
Performs Conover's non-parametric many-to-one comparison test for Kruskal-type ranked data.
kwManyOneConoverTest(x, ...) ## Default S3 method: kwManyOneConoverTest( x, g, alternative = c("two.sided", "greater", "less"), p.adjust.method = c("single-step", p.adjust.methods), ... ) ## S3 method for class 'formula' kwManyOneConoverTest( 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 Conover'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: 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 t distribution. Otherwise,
the p-values are computed from the t-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.
Conover, W. J, Iman, R. L. (1979) On multiple-comparisons procedures, Tech. Rep. LA-7677-MS, Los Alamos Scientific Laboratory.
## 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)
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