# 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

 `1` ```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 <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.

`qvSUDT`

## Examples

 `1` ```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.