data("RProjects", package = "ReplicationSuccess")
## Computing key quantities
RProjects$zo <- RProjects$fiso/RProjects$se_fiso
RProjects$zr <- RProjects$fisr/RProjects$se_fisr
RProjects$c <- RProjects$se_fiso^2/RProjects$se_fisr^2
## Computing one-sided p-values for alternative = "greater"
RProjects$po1 <- z2p(z = RProjects$zo, alternative = "greater")
RProjects$pr1 <- z2p(z = RProjects$zr, alternative = "greater")
## Plots of effect estimates
parOld <- par(mfrow = c(2, 2))
for (p in unique(RProjects$project)) {
data_project <- subset(RProjects, project == p)
plot(rr ~ ro, data = data_project, ylim = c(-0.5, 1),
xlim = c(-0.5, 1), main = p, xlab = expression(italic(r)[o]),
ylab = expression(italic(r)[r]))
abline(h = 0, lty = 2)
abline(a = 0, b = 1, col = "grey")
}
par(parOld)
## Plots of peer beliefs
RProjects$significant <- factor(RProjects$pr < 0.05,
levels = c(FALSE, TRUE),
labels = c("no", "yes"))
parOld <- par(mfrow = c(1, 2))
for (p in c("Experimental Economics", "Social Sciences")) {
data_project <- subset(RProjects, project == p)
boxplot(pm_belief ~ significant, data = data_project, ylim = c(0, 1),
main = p, xlab = "Replication effect significant", ylab = "Peer belief")
stripchart(pm_belief ~ significant, data = data_project, vertical = TRUE,
add = TRUE, pch = 1, method = "jitter")
}
par(parOld)
## Computing the sceptical p-value
ps <- with(RProjects, pSceptical(zo = fiso/se_fiso,
zr = fisr/se_fisr,
c = se_fiso^2/se_fisr^2))
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