# DFDR.p.adjust: Adjusted P-Values for the Double FDR Procedure In allenzhuaz/MHTmult: Multiple Hypotheses Testing for Multiple Families/Groups Structure

## Description

Given a list/data frame of grouped p-values, retruns adjusted p-values to make decisions

## Usage

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

## Arguments

 `pval` the structural p-values, the type should be `"list"`. `t` the threshold selecting significant families. `make.decision` logical; if `TRUE`, then the output include the decision rules compared adjusted p-values with significant level α. `alpha` significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.

## Value

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

Yalin Zhu

## References

Mehrotra, D. V., & Heyse, J. F. (2004). Use of the false discovery rate for evaluating clinical safety data. Statistical methods in medical research, 13: 227-238.

`DFDR2.p.adjust`, `p.adjust`.
 ```1 2 3 4 5 6 7 8 9``` ```# 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)) DFDR.p.adjust(pval = pval,t=0.1) sum(unlist(DFDR.p.adjust(pval = pval,t=0.1))<=0.1) ```