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PMCMRplus (version 1.9.3)

normalScoresAllPairsTest: Lu-Smith All-Pairs Comparison Normal Scores Test

Description

Performs Lu-Smith all-pairs comparison normal scores test.

Usage

normalScoresAllPairsTest(x, ...)

# S3 method for default normalScoresAllPairsTest( x, g, p.adjust.method = c("single-step", p.adjust.methods), ... )

# S3 method for formula normalScoresAllPairsTest( formula, data, subset, na.action, p.adjust.method = c("single-step", p.adjust.methods), ... )

Arguments

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 "x". Ignored with a warning if "x" is a list.

p.adjust.method

method for adjusting p values (see p.adjust).

formula

a formula of the form response ~ group where response gives the data values and group a vector or factor of the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

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 NAs. Defaults to getOption("na.action").

Value

A list with class "PMCMR" containing the following components:

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

statistic

lower-triangle matrix of the estimated quantiles of the pairwise test statistics.

p.value

lower-triangle matrix of the p-values for the pairwise tests.

alternative

a character string describing the alternative hypothesis.

p.adjust.method

a character string describing the method for p-value adjustment.

model

a data frame of the input data.

dist

a string that denotes the test distribution.

Details

For all-pairs comparisons in an one-factorial layout with non-normally distributed residuals Lu and Smith's normal scores transformation can be used prior to an all-pairs comparison test. A total of \(m = k(k-1)/2\) hypotheses can be tested. The null hypothesis H\(_{ij}: F_i(x) = F_j(x)\) is tested in the two-tailed test against the alternative A\(_{ij}: F_i(x) \ne F_j(x), ~~ i \ne j\). For p.adjust.method = "single-step" the Tukey's studentized range distribution is used to calculate p-values (see Tukey). Otherwise, the t-distribution is used for the calculation of p-values with a latter p-value adjustment as performed by p.adjust.

References

Lu, H., Smith, P. (1979) Distribution of normal scores statistic for nonparametric one-way analysis of variance. Journal of the American Statistical Association 74, 715--722.

See Also

normalScoresTest, normalScoresManyOneTest, normOrder.