find.sample.size: Determines the sample size needed to achieve the desired FDR...

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

Determines the sample size needed to achieve the desired FDR and average power

Description

Determines the sample size needed to achieve the desired FDR and average power by given the proportion of true null hypothesis.

Usage

find.sample.size(alpha, pwr, avepow.func, n0 = 3, n1 = 6, max.its = 50, ...)

Arguments

alpha	the fixed p-value threshold (scalar numeric)
pwr	desired average power (scalar numeric)
avepow.func	an R function to compute average power
n0	lower limit for initial sample size range
n1	upper limit for initial sample size range
max.its	maximum number of iterations
...	additional arguments to average power function

Value

A list with the following components:

n	a sample size estimate
computed.avepow	average power
desired.avepow	desired average power
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

Note

For the test with power calculation based on asymptotic normal approximation, we suggest checking FDRsamplesize2 calculation by simulation.

Examples

#Here, calculating the sample size for the study involving many sign tests
average.power.signtest;
p.adj = 0.001;
p = rep(c(0.8,0.5), c(100,9900));
find.sample.size(alpha = p.adj, pwr = 0.8, avepow.func = average.power.signtest, p = p)

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