PMCMRplus (version 1.6.0)

dunnettT3Test: Dunnett's T3 Test

Description

Performs Dunnett's all-pairs comparison test for normally distributed data with unequal variances.

Usage

dunnettT3Test(x, ...)
# S3 method for default
dunnettT3Test(x, g, ...)
# S3 method for formula
dunnettT3Test(formula, data, subset, na.action, ...)
# S3 method for aov
dunnettT3Test(x, ...)

Arguments

x

a numeric vector of data values, a list of numeric data vectors or a fitted model object, usually an aov fit.

…

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.

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 normally distributed residuals but unequal groups variances the T3 test of Dunnett can be performed. A total of \(m = k(k-1)/2\) hypotheses can be tested. The null hypothesis H\(_{ij}: \mu_i(x) = \mu_j(x)\) is tested in the two-tailed test against the alternative A\(_{ij}: \mu_i(x) \ne \mu_j(x), ~~ i \ne j\).

The p-values are computed from the studentized maximum modulus distribution that is the equivalent of the multivariate t distribution with \(\rho = 0\). The function pmvt is used to calculate the \(p\)-values.

References

C. W. Dunnett (1980) Pair wise multiple comparisons in the unequal variance case, Journal of the American Statistical Association 75, 796--800.

See Also

pmvt

Examples

# NOT RUN {
fit <- aov(weight ~ feed, chickwts)
shapiro.test(residuals(fit))
bartlett.test(weight ~ feed, chickwts)
anova(fit)

## also works with fitted objects of class aov
res <- dunnettT3Test(fit)
summary(res)
summaryGroup(res)

# }