fit2df.stanfit = function(fit, X, condense=TRUE, metrics=FALSE, na.to.missing = TRUE, estimate.suffix=""){
stanfit = fit
pars = "beta"
quantiles = c(0.025, 0.50, 0.975)
# Extract model
explanatory = attr(X, "dimnames")[[2]]
model = rstan::summary(stanfit,
pars = pars,
probs = quantiles)$summary
or = round(exp(model[, 1]), 2)
L95 = round(exp(model[, 4]), 2)
U95 = round(exp(model[, 6]), 2)
# Determine a p-value based on two-sided examination of chains
chains = rstan::extract(stanfit, pars=pars, permuted = TRUE, inc_warmup = FALSE,
include = TRUE)
p1.out = apply(chains[[1]], 2, function(x)mean(x<0))
p2.out = apply(chains[[1]], 2, function(x)mean(x>0))
p1.out = p1.out*2
p2.out = p2.out*2
p.out = ifelse(p1.out < 1, p1.out, p2.out)
p = round(p.out, 3)
df.out = data.frame(explanatory, or, L95, U95, p)
colnames(df.out) = c("explanatory", paste0("OR", estimate.suffix), "L95", "U95", "p")
# Remove intercept
df.out = df.out[-which(df.out$explanatory =="(Intercept)"),]
# Condensed output (now made default)
if (condense==TRUE){
p = paste0("=", sprintf("%.3f", df.out$p))
p[p == "=0.000"] = "<0.001"
df.out = data.frame(
"explanatory" = df.out$explanatory,
"OR" = paste0(sprintf("%.2f", df.out$OR), " (", sprintf("%.2f", df.out$L95), "-",
sprintf("%.2f", df.out$U95), ", p", p, ")"))
colnames(df.out) = c("explanatory", paste0("OR", estimate.suffix))
}
# Extract model metrics
if (metrics==TRUE){
# n_data = dim(x$data)[1] # no equivalent here
n_model = dim(X)[1]
# aic = round(x$aic, 1) # add WAIC later?
# auc = round(roc(x$y, x$fitted)$auc[1], 3) # Add predicted mu later?
metrics.out = paste0(
# "Number in dataframe = ", n_data,
", Number in model = ", n_model)
# ", Missing = ", n_data-n_model,
# ", AIC = ", aic,
# ", C-statistic = ", auc)
}
if (metrics==TRUE){
return(list(df.out, metrics.out))
} else {
return(df.out)
}
return(df.out)
}
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