STAREG (version 1.0.3)

stareg: An empirical Bayes approach for replicability analysis across two studies

Description

An empirical Bayes approach for replicability analysis across two studies

Usage

stareg(pa, pb, init.pi0 = TRUE)

Value

A list:

Lfdr

The estimated local false discovery rate for replicability null.

fdr

The adjusted Lfdr values based on the step-up procedure for FDR control.

xi00

An estimate of the prior probability for joint state (0, 0) in two studies.

xi01

An estimate of the prior probability for joint state (0, 1) in two studies.

xi10

An estimate of the prior probability for joint state (1, 0) in two studies.

xi11

An estimate of the prior probability for joint state (1, 1) in two studies.

f1

A non-parametric estimate for the non-null probability density function in study 1.

f2

A non-parametric estimate for the non-null probability density function in study 2.

Arguments

pa

A numeric vector of p-values from study 1.

pb

A numeric vector of p-values from study 2.

init.pi0

A logistic value for deciding whether to initialize the prior probabilities based on the estimates of pi0's. If true, estimate the marginal pi0's in two studies using qvalue; otherwise, specify pi0_pa = pi_pb = 0.9.

Examples

# Simulate p-values in two studies
m = 10000
h = sample(0:3, m, replace = TRUE, prob = c(0.9, 0.025, 0.025, 0.05))
states1 = rep(0, m); states2 = rep(0, m)
states1[which(h==2|h==3)] = 1; states2[which(h==1|h==3)] = 1
z1 = rnorm(m, states1*2, 1)
z2 = rnorm(m, states2*3, 1)
p1 = 1 - pnorm(z1); p2 = 1 - pnorm(z2)
# Run STAREG to identify replicable signals
res.stareg = stareg(p1, p2)
sig.idx = which(res.stareg$fdr <= 0.05)