Nothing
# compute fits for the HN0 and J benchmarks and the actual priors specified
fit_models_RA <- function (df, tau.prior = list(), scale.hn0 = 1/500,
mu.mean = 0, mu.sd = 4, interval.type = "central") {
# compute the posterior for the Jefferys prior
fit.j <- bayesmeta(y = df[, "y"], sigma = df[, "sigma"],
mu.prior.mean = mu.mean, mu.prior.sd = mu.sd, tau.prior = "Jeffreys",
interval.type= interval.type)
# compute the posterior for the HN0 = HN(0, 0.01) prior
# tau_sd_AA0 <- 1/500
# tau_sd_AA0 <- 1/100
fit.hn0 <- bayesmeta(y = df[, "y"], sigma = df[, "sigma"],
mu.prior.mean = mu.mean, mu.prior.sd = mu.sd,
tau.prior = function (x) dhalfnormal(x, scale = scale.hn0),
interval.type= interval.type)
if (length(tau.prior) > 0) {
n.act <- length(tau.prior)
fit.actual <- list()
fit.bms <- list(fit.hn0, fit.j)
for (i in 1:n.act) {
fit.actual[[i]] <- bayesmeta(y = df[, "y"],
sigma = df[, "sigma"], mu.prior.mean = mu.mean,
mu.prior.sd = mu.sd, tau.prior = tau.prior[[i]],
interval.type= interval.type)
# interval.type="central")
}
fits <- c(fit.bms, fit.actual)
names.fit.actual <- rep(NA, times = n.act)
for (j in 1:n.act) {
names.fit.actual[j] <- paste0("fit.actual_",
j, sep = "")
}
names(fits) <- c("fit.hn0", "fit.j", names.fit.actual)
return(fits)
}
else fits <- list(fit.hn0 = fit.hn0, fit.j = fit.j)
return(fits)
}
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