| stareg {STAREG} | R Documentation |
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)
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. |
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. |
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)