Dunn's All-Pairs Rank Comparison Test
Performs Dunn's non-parametric all-pairs comparison test for Kruskal-type ranked data.
kwAllPairsDunnTest(x, ...) ## Default S3 method: kwAllPairsDunnTest(x, g, p.adjust.method = p.adjust.methods, ...) ## S3 method for class 'formula' kwAllPairsDunnTest( 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 all-pairs comparisons in an one-factorial layout with non-normally distributed residuals Dunn's non-parametric test can be performed. A total of m = k(k-1)/2 hypotheses can be tested. The null hypothesis H_{ij}: μ_i(x) = μ_j(x) is tested in the two-tailed test against the alternative A_{ij}: μ_i(x) \ne μ_j(x), ~~ i \ne j.
The p-values are computed from the standard normal distribution using
any of the p-adjustment methods as included in p.adjust.
Originally, Dunn (1964) proposed Bonferroni's p-adjustment method.
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.
Dunn, O. J. (1964) Multiple comparisons using rank sums, Technometrics 6, 241–252.
Siegel, S., Castellan Jr., N. J. (1988) Nonparametric Statistics for The Behavioral Sciences. New York: McGraw-Hill.
## Data set InsectSprays
## Global test
kruskalTest(count ~ spray, data = InsectSprays)
## Conover's all-pairs comparison test
## single-step means Tukey's p-adjustment
ans <- kwAllPairsConoverTest(count ~ spray, data = InsectSprays,
p.adjust.method = "single-step")
summary(ans)
## Dunn's all-pairs comparison test
ans <- kwAllPairsDunnTest(count ~ spray, data = InsectSprays,
p.adjust.method = "bonferroni")
summary(ans)
## Nemenyi's all-pairs comparison test
ans <- kwAllPairsNemenyiTest(count ~ spray, data = InsectSprays)
summary(ans)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.