GTBH.p.adjust: The adjusted p-values for Gilbert-Tarone-BH step-up FDR...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/FDRSU.R

Description

The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.

Usage

1
GTBH.p.adjust(p, p.set, alpha, make.decision)

Arguments

p

numeric vector of p-values (possibly with NAs). Any other R is coerced by as.numeric. Same as in p.adjust.

p.set

a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.

alpha

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

make.decision

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

Value

A numeric vector of the adjusted p-values (of the same length as p).

Author(s)

Yalin Zhu

References

Gilbert, P. B. (2005). A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54: 143-158.

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57: 289-300.

See Also

GTBY.p.adjust, MBH.p.adjust, MBY.p.adjust

Examples

1
2
3
p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))
GTBH.p.adjust(p,p.set)

Example output

[1] 0.04096 0.04096 0.04096

MHTdiscrete documentation built on Nov. 23, 2017, 9:03 a.m.