regwq: REGWQ - Ryan / Einot and Gabriel / Welsch test procedure...

In mutoss: Unified Multiple Testing Procedures

REGWQ - Ryan / Einot and Gabriel / Welsch test procedure...

Description

REGWQ - Ryan / Einot and Gabriel / Welsch test procedure This function computes REGWQ test for given data including p samples. It is based on a stepwise or layer approach to significance testing. Sample means are ordered from the smallest to the largest. The largest difference, which involves means that are r = p steps apart, is tested first at \alpha level of significance; if significant, means that are r <p steps apart are tested at a different \alpha level of significance and so on. Compare to the Student- Newman-Keuls test, the \alpha levels are adjusted for the p-1 different layers by the formula \alpha_p=\alpha, if p=k or p=k-1, \alpha_p = 1-(1-\alpha)^{p/k} otherwise. It might happen that the quantiles are not descending in p. In this case, they are adapted by c_k = max_{2\leq r \leq k} c_r, k=2,\ldots,p. The REGWQ procedure, like Tukey's procedure, requires equal sample n's. However, in this algorithm, the procedure is adapted to unequal sample sized which can lead to still conservative test decisions.

Usage

regwq(formula, data, alpha, MSE=NULL, df=NULL, silent=FALSE)

Arguments

formula	Formula defining the statistical model containing the response and the factors
data	dataset containing the response and the grouping factor
alpha	The level at which the error should be controlled. By default it is alpha=0.05.
MSE	Optional for a given variance of the data
df	Optional for a given degree of freedom
silent	If true any output on the console will be suppressed.

Value

A list containing:

adjPValues	A numeric vector containing the adjusted pValues
rejected	A logical vector indicating which hypotheses are rejected
statistics	A numeric vector containing the test-statistics
confIntervals	A matrix containing only the estimates
errorControl	A Mutoss S4 class of type errorControl, containing the type of error controlled by the function.

Author(s)

Frank Konietschke

References

Hochberg, Y. & Tamhane, A. C. (1987). Multiple Comparison Procedures, Wiley.

Examples

x = rnorm(50)
grp = c(rep(1:5,10))
dataframe <- data.frame(x,grp)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = TRUE)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = FALSE)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=1, df=Inf, silent = FALSE) # known variance
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=1, df=1000, silent = FALSE) # known variance

mutoss documentation built on March 31, 2023, 8:46 p.m.