avgFDR.p.adjust {MHTmult}

Adjusted P-Values for Benjamini & Bogomolov (2014) BH-q BH-Rq/m Procedure

Description

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

Usage

avgFDR.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 \alpha.

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.

Author(s)

Yalin Zhu

References

Benjamini, Y., & Bogomolov, M. (2014). Selective inference on multiple families of hypotheses. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76: 297-318.

Examples

# 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))
avgFDR.p.adjust(pval = pval, t=0.1)
sum(unlist(avgFDR.p.adjust(pval = pval,t=0.1)) <= 0.1)