DFDR.p.adjust: Adjusted P-Values for the Double FDR Procedure

In 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

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.

Author(s)

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.

See Also

DFDR2.p.adjust, p.adjust.

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

Example output

[[1]]
 [1] 0.05166667 0.05166667 0.05166667 0.02500000 0.05166667 0.00000000
 [7] 0.87400000 0.44333333 0.36625000 0.11000000

[[2]]
NULL

[[3]]
[1] 1.000 1.000 1.000 1.000 0.066 1.000

[[4]]
[1] 0.889 0.889 0.889 0.045 0.889

[[5]]
 [1] 1.0000000 0.9716667 0.7233333 0.9862500 0.7233333 1.0000000 0.2000000
 [8] 0.9862500 0.9716667 0.9716667

[[6]]
NULL

[1] 8

MHTmult documentation built on May 2, 2019, 9:11 a.m.