NemenyiTest {DescTools}  R Documentation 
Performs Nemenyi's test of multiple comparisons.
NemenyiTest(x, ...)
## Default S3 method:
NemenyiTest(x, g, dist = c("tukey", "chisq"), out.list = TRUE, ...)
## S3 method for class 'formula'
NemenyiTest(formula, data, subset, na.action, ...)
x 
a numeric vector of data values, or a list of numeric data vectors. 
g 
a vector or factor object giving the group for the
corresponding elements of 
dist 
the distribution used for the test. Can be 
out.list 
logical, defining if the output should be organized in listform. 
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 
... 
further arguments to be passed to or from methods. 
Nemenyi proposed a test based on rank sums and the application of the familywise error method to control Type I error inflation, if multiple comparisons are done. The Tukey and Kramer approach uses mean rank sums and can be employed for equally as well as unequally sized samples without ties.
A list of class htest
, containing the following components:
statistic 
Nemenyi test 
p.value 
the pvalue for the test 
null.value 
is the value of the median specified by the null hypothesis. This
equals the input argument 
alternative 
a character string describing the alternative hypothesis. 
method 
the type of test applied 
data.name 
a character string giving the names of the data. 
Andri Signorell <andri@signorell.net>
Nemenyi, P. B. (1963) DistributionFree Multiple Comparisons New York, State University of New York, Downstate Medical Center
Hollander, M., Wolfe, D.A. (1999) Nonparametric Statistical Methods New York, Wiley, pp. 787
Friedman, M. (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance Journal of the American Statistical Association, 32:675701
Friedman, M. (1940) A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics, 11:8692
## Hollander & Wolfe (1973), 116.
## Mucociliary efficiency from the rate of removal of dust in normal
## subjects, subjects with obstructive airway disease, and subjects
## with asbestosis.
x < c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
y < c(3.8, 2.7, 4.0, 2.4) # with obstructive airway disease
z < c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
NemenyiTest(list(x, y, z))
## Equivalently,
x < c(x, y, z)
g < factor(rep(1:3, c(5, 4, 5)),
labels = c("Normal subjects",
"Subjects with obstructive airway disease",
"Subjects with asbestosis"))
NemenyiTest(x, g)
## Formula interface.
boxplot(Ozone ~ Month, data = airquality)
NemenyiTest(Ozone ~ Month, data = airquality)
# Hedderich & Sachs, 2012, p. 555
d.frm < data.frame(x=c(28,30,33,35,38,41, 36,39,40,43,45,50, 44,45,47,49,53,54),
g=c(rep(LETTERS[1:3], each=6)), stringsAsFactors=TRUE)
NemenyiTest(x~g, d.frm)