cvSDDT {DunnettTests}R Documentation

To calculate the critical constants for step-down Dunnett test procedure

Description

The function applies to testing problem with either t distributed test statistics or (approximately) normally distributed test statistics. The function accomodates both equally correlated and unequally correlated test statistics.

Usage

cvSDDT(k,alpha=0.05,alternative="U",df = Inf,corr = 0.5,corr.matrix=NA)

Arguments

k

Number of hypotheses to be tested, k≥ 2 and k≤ 16.

alpha

The pre-specified overall significance level, default=0.05.

alternative

The alternative hypothesis: "U"=upper one-sided test (default); "B"=two-sided test. For lower one-sided tail test, specify alternative="U" and use the negations of the return critical constants.

df

Degree of freedom of the t-test statistics. When (approximately) normally distributed test statistics are applied, set df=Inf (default).

corr

Specified for equally correlated test statistics, which is the common correlation between the test statistics, default=0.5.

corr.matrix

Specified for unequally correlated test statistics, which is the correlation matrix of the test statistics, default=NA.

Value

Return a k-vector of critical constants from smallest to largest.

Author(s)

FAN XIA <phoebexia@yahoo.com>

References

Charles W Dunnett and Ajit C Tamhane. Step-down multiple tests for comparing treatments with a control in unbalanced one-way layouts. Statistics in Medicine, 10(6):939-947, 1991.

Examples

#To test four hypotheses, the test statistics are 
#2.2 (H1), 2.7 (H2), 2.1(H3), 0.85(H4), respectively. 
#The test statistcis are equally correlated at 0.6 and have df=30. 
#At overall one-sided significance level 0.05, the critical constants are given by:

cvSDDT(k=4,df=30,corr=0.6)

#based on the critical values, we reject H2, H1, H3 in a sequence and accept H4.

[Package DunnettTests version 2.0 Index]