mutoss (version 0.1-9)

regwq: 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

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.

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}