# GBH.p.adjust: Adjusted P-values for the Group BH Procedure In allenzhuaz/MHTmult: Multiple Hypotheses Testing for Multiple Families/Groups Structure

## Description

Given a list/data frame of grouped p-values, selecting thresholds and p-value combining method, retruns adjusted conditional p-values to make decisions

## Usage

 `1` ```GBH.p.adjust(pval, t, make.decision) ```

## Arguments

 `pval` the structural p-values, the type should be `"list"`. `t` the thresholds determining whether the families are selected or not, also affects conditional p-value within families. `make.decision` logical; if `TRUE`, then the output include the decision rules compared adjusted p-values with significant level α

## Value

A list of the adjusted conditional p-values, a list of `NULL` means the family is not selected to do the test in the second stage.

Yalin Zhu

## References

Hu, J. X., Zhao, H., & Zhou, H. H. (2010). False discovery rate control with groups. Journal of the American Statistical Association, 105: 1215-1227.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```# data is from Example 4.1 in Mehrotra and Adewale (2012) pval <- list(c(0.031,0.023,0.029,0.005,0.031,0.000,0.874,0.399,0.293,0.077), c(0.216,0.843,0.864), c(1,0.878,0.766,0.598,0.011,0.864), c(0.889,0.557,0.767,0.009,0.644), c(1,0.583,0.147,0.789,0.217,1,0.02,0.784,0.579,0.439), c(0.898,0.619,0.193,0.806,0.611,0.526,0.702,0.196)) sum(p.adjust(unlist(pval), method = "BH")<=0.1) DFDR.p.adjust(pval = pval,t=0.1) DFDR2.p.adjust(pval = pval,t=0.1) sum(unlist(DFDR.p.adjust(pval = pval,t=0.1))<=0.1) sum(unlist(DFDR2.p.adjust(pval = pval,t=0.1))<=0.1) GBH.p.adjust(pval = pval,t=0.1) sum(unlist(GBH.p.adjust(pval = pval,t=0.1))<=0.1) t=select.thres(pval,select.method = "BH", comb.method = "minP", alpha = 0.1) cFDR.cp.adjust(pval, t=t, comb.method="minP") t1=select.thres(pval, select.method = "bonferroni", comb.method = "minP", alpha = 0.1, k=3) cFDR.cp.adjust(pval, t=t1, comb.method="minP") t2=select.thres(pval, select.method = "sidak", comb.method = "minP", alpha = 0.1, k=3) cFDR.cp.adjust(pval, t=t2, comb.method="minP") ```

allenzhuaz/MHTmult documentation built on June 1, 2017, 5:22 p.m.