# getPval: Calculating p-values for discrete data In MHTdiscrete: Multiple Hypotheses Testing for Discrete Data

## Description

The function for calculating the original available p-values and all attaianble p-values for the corresponding hypothesis.

## Usage

 `1` ```getPval(raw.data, test.type, alternative) ```

## Arguments

 `raw.data` original data set with count number for treatment group and study group. The data set type could be `matrix` or `data.frame`. `test.type` there are two discrete test available now, must be one of `"FET"` for Fisher's Exact Test and `"BET"` for Binomial Exact Test. `alternative` indicates the alternative hypothesis and must be one of `"two.sided"`, `"greater"` or `"less"`.

## Value

A numeric vector of the adjusted p-values (of the same length as p).

Yalin Zhu

## References

Zhu, Y., & Guo, W. (2017). Familywise error rate controlling procedures for discrete data arXiv preprint arXiv:1711.08147.

Clopper, C. J. & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26: 404-413.

Fisher, R. A. (1922). On the Interpretation of χ^2 from Contingency Tables, and the Calculation of P. Journal of the Royal Statistical Society, 85: 87-94.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ``` ## Using Fisher's Exact Test to get the avaiable and attainablep-values # import raw data set as data.frame type df <- data.frame(X1=c(4, 2, 2, 13, 6, 8, 4, 0, 1), N1 = rep(148, 9), X2 = c(0, 0, 1, 3, 2, 1, 2, 2, 2), N2 = rep(132, 9)) # obtain the avaiable p-values and attainable p-values using two-sided Fisher's Exact Test getPval(raw.data=df, test.type = "FET",alternative = "two.sided") # store the avaiable p-values p <- getPval(raw.data=df, test.type = "FET",alternative = "two.sided")[[1]]; p # store the attainable p-values p.set <- getPval(raw.data=df, test.type = "FET",alternative = "two.sided")[[2]]; p.set ```

MHTdiscrete documentation built on May 1, 2019, 10:23 p.m.