cFDR.cp.adjust {MHTmult}

Adjusted Conditional P-values for Two-stage cFDR Controlling Procedures

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

cFDR.cp.adjust(pval, t, comb.method = c("Fisher", "Stouffer", "minP"),
make.decision, sig.level)

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.

comb.method

p-value combining methods including "Fisher", "Stouffer", and "minP" combining methods.

make.decision

logical; if TRUE, then the output include the decision rules compared adjusted p-values with significant level \alpha.

sig.level

significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.

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

Heller, R., Chatterjee, N., Krieger, A., & Shi, J. (2016). Post-selection Inference Following Aggregate Level Hypothesis Testing in Large Scale Genomic Data. bioRxiv, 058404.

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))
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)

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")