qvSDDT: To calculate adjusted P-values (Q-values) for step-down...

In DunnettTests: Software implementation of step-down and step-up Dunnett test procedures

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

qvSDDT(teststats,alternative="U",df=Inf,corr = 0.5,corr.matrix = NA)

Arguments

teststats	The k-vector of test statistics, k≥ 2.
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.

Value

Return a LIST containing:

"ordered test statistics"	ordered test statistics from largest to smallest
"Adjusted P-values of ordered test statistics"	adjusted P-values correspond to the ordered test statistics

Author(s)

FAN XIA <[e-mail]>

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

qvSUDT

Examples

qvSDDT(c(2.20,2.70,2.10,0.85),df=30)

Example output

Loading required package: mvtnorm
$`ordered test statistics`
  H2   H1   H3   H4 
2.70 2.20 2.10 0.85 

$`Adjusted P-values of ordered test statistics`
[1] 0.019 0.045 0.045 0.201

DunnettTests documentation built on May 2, 2019, 9:13 a.m.