predictionInterval(thetao = c(1.5, 2, 5), seo = 1, ser = 0.5, designPrior = "EB")
# compute prediction intervals for replication projects
data("RProjects", package = "ReplicationSuccess")
parOld <- par(mfrow = c(2, 2))
for (p in unique(RProjects$project)) {
data_project <- subset(RProjects, project == p)
PI <- predictionInterval(thetao = data_project$fiso, seo = data_project$se_fiso,
ser = data_project$se_fisr)
PI <- tanh(PI) # transforming back to correlation scale
within <- (data_project$rr < PI$upper) & (data_project$rr > PI$lower)
coverage <- mean(within)
color <- ifelse(within == TRUE, "#333333B3", "#8B0000B3")
study <- seq(1, nrow(data_project))
plot(data_project$rr, study, col = color, pch = 20,
xlim = c(-0.5, 1), xlab = expression(italic(r)[r]),
main = paste0(p, ": ", round(coverage*100, 1), "% coverage"))
arrows(PI$lower, study, PI$upper, study, length = 0.02, angle = 90,
code = 3, col = color)
abline(v = 0, lty = 3)
}
par(parOld)
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