regwq: REGWQ - Ryan / Einot and Gabriel / Welsch test procedure... In mutoss: Unified Multiple Testing Procedures

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 α level of significance; if significant, means that are r <p steps apart are tested at a different α level of significance and so on. Compare to the Student- Newman-Keuls test, the α levels are adjusted for the p-1 different layers by the formula α_p=α, if p=k or p=k-1, α_p = 1-(1-α)^{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≤q r ≤q k} c_r, k=2,…,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

 1 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

 1 2 3 4 5 6 7 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 May 2, 2019, 2:38 a.m.