alpha.power.fdr {FDRsamplesize2}

Compute p-value threshold for given the proportion pi0 of tests with a true null

Description

Given the proportion pi0 of tests with a true null, find the p-value threshold that results in a desired FDR and average power.

Usage

alpha.power.fdr(fdr, pwr, pi0, method = "HH")

Arguments

fdr

desired FDR (scalar numeric)

pwr

desired average power (scalar numeric)

pi0

the proportion of tests with a true null hypothesis

method

method to estimate proportion pi0 of tests with true null, including: "HH" (p-value histogram height) , "HM" (p-value histogram mean), "BH" (Benjamini & Hochberg 1995), "Jung" (Jung 2005)

Details

To get the fixed p-value threshold for multiple testing procedure, 4 approximation methods are provided, they are Benjamini & Hochberg procedure (1995), Jung's formula (2005), method of using p-value histogram height (HH) and method of using p-value histogram mean (HM). For last two methods' details, see Ni Y, Onar-Thomas A, Pounds S. "Computing Power and Sample Size for the False Discovery Rate in Multiple Applications"

Value

The fixed p-value threshold for multiple testing procedure

References

Pounds S and Cheng C, "Sample size determination for the false discovery rate." Bioinformatics 21.23 (2005): 4263-4271.

Gadbury GL, et al. (2004) Power and sample size estimation in high dimensional biology. Statistical Methods in Medical Research 13(4):325-38.

Jung,Sin-Ho."Sample size for FDR-control in microarray data analysis." Bioinformatics 21.14 (2005): 3097-3104.

Ni Y, Seffernick A, Onar-Thomas A, Pounds S. "Computing Power and Sample Size for the False Discovery Rate in Multiple Applications", Manuscript.

Examples

alpha.power.fdr(fdr = 0.1, pwr = 0.9, pi0=0.9, method = "HH")