n.fdr.fisher: Sample size calculation for Fisher's Exact tests

In FDRsamplesize2: Computing Power and Sample Size for the False Discovery Rate in Multiple Applications

Sample size calculation for Fisher's Exact tests

Description

Find the sample size needed to have a desired false discovery rate and average power for a large number of Fisher's exact tests.

Usage

n.fdr.fisher(fdr, pwr, p1, p2, alternative = "two.sided", pi0.hat = "BH")

Arguments

fdr	desired FDR (scalar numeric)
pwr	desired average power (scalar numeric)
p1	probability in one group (vector)
p2	probability in other group (vector)
alternative	one- or two-sided test
pi0.hat	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)

Value

A list with the following components:

n	per-group sample size estimate
computed.avepow	average power
desired.avepow	desired average power
desired.fdr	desired FDR
input.pi0	proportion of tests with a true null hypothesis
alpha	fixed p-value threshold for multiple testing procedure
n.its	number of iteration
max.its	maximum number of iteration, default is 50
n0	lower limit for initial sample size range
n1	upper limit for initial sample size range

Examples

set.seed(1234);
p1 = sample(seq(0,0.5,0.1),10,replace = TRUE);
p2 = sample(seq(0.5,1,0.1),10,replace = TRUE);
n.fdr.fisher(fdr = 0.1, pwr = 0.8, p1 = p1, p2 = p2, alternative = "two.sided", pi0.hat = "BH")

FDRsamplesize2 documentation built on May 29, 2024, 7:12 a.m.