qvSUDT {DunnettTests}R Documentation

To calculate adjusted P-values (Q-values) for step-up Dunnett test procedure.

Description

In multiple testing problem, the adjusted P-values correspond to test statistics can be used with any fixed alpha to dertermine which hypotheses to be rejected.

Usage

qvSUDT(teststats,alternative="U",df=Inf,corr=0.5,corr.matrix=NA,mcs=1e+05)

Arguments

teststats

The k-vector of test statistics, k≥ 2 and k≤ 16.

alternative

The alternative hypothesis: "U"=upper one-sided test (default); "L"=lower one-sided test; "B"=two-sided test. For lower one-sided tail test, use the negations of each of the test statistics.

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.

mcs

The number of monte carlo sample points to numerically approximate the probability that to solve critical values for a given P value, refer to Equation (3.3) in Dunnett and Tamhane (1992), default=1e+05.

Value

Return a LIST containing:

"ordered test statistics"

ordered test statistics from smallest to largest

"Adjusted P-values of ordered test statistics"

adjusted P-values correspond to the ordered test statistics

Author(s)

FAN XIA <phoebexia@yahoo.com>

References

Charles W. Dunnett and Ajit C. Tamhane. A step-up multiple test procedure. Journal of the American Statistical Association, 87(417):162-170, 1992.

See Also

qvSDDT

Examples

qvSUDT(c(2.20,2.70),df=30)

[Package DunnettTests version 2.0 Index]