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.

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