FDRsamplesize2 (version 0.2.0)

fdr.avepow: Compute FDR and average power for a given sample size and effect size vector

Description

For a given fixed sample size and effect size vector,compute FDR and average power as a function of the p-value threshold alpha.

Usage

fdr.avepow(n, avepow.func, null.hypo, alpha = 1:100/1000, method = "BH", ...)

Value

A list with the following components:

n

input sample size

avepow.func

average power function

null.hypo

null hypothesis string

pi0

computed value of pi0

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)

other.args

additional arguments

res.tbl

table of alpha, fdr, and average power

Arguments

n

sample size

avepow.func

function to compute average power

null.hypo

string to evaluate null hypothesis

alpha

p-value threshold(s) to consider

method

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

...

additional arguments, including effect size vector for average power function

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

n = 50; # number of events
logHR = rep(c(0,0.5),c(950,50));
v = rep(1,length(logHR));  # variance of predictor variable (vector)
res = fdr.avepow(n,average.power.coxph,"logHR==0",logHR=logHR,v=v);
res$pi0;
head(res$res.tbl)