# Wrapper for lmmixed
fit2df.lmerMod = function(fit, condense=TRUE, metrics=FALSE, estimate.suffix=""){
x = fit
explanatory = names(lme4::fixef(x))
coef = round(lme4::fixef(x), 2)
ci = round(lme4::confint.merMod(x, method='Wald'), 2)
ci = ci[-grep("sig", rownames(ci)),]
p = round(1-pnorm(abs(summary(x)$coefficients[,3])), 3) # WARNING! Simple conversion of t- to p-values assuming infinite df
warning("P-value for lmer is estimate assuming t-distribution is normal. Bootstrap for final publication.")
df.out = data.frame(explanatory, coef, ci[,1], ci[,2], p)
colnames(df.out) = c("explanatory", paste0("Coefficient", estimate.suffix), "L95", "U95", "p")
# Remove intercepts
df.out = df.out[-which(df.out$explanatory =="(Intercept)"),]
if (condense==TRUE){
p = paste0("=", sprintf("%.3f", df.out$p))
p[p == "=0.000"] = "<0.001"
df.out = data.frame(
"explanatory" = df.out$explanatory,
"Coefficient" = paste0(sprintf("%.2f", df.out$Coefficient), " (", sprintf("%.2f", df.out$L95), " to ",
sprintf("%.2f", df.out$U95), ", p", p, ")"))
colnames(df.out) = c("explanatory", paste0("Coefficient", estimate.suffix))
}
# Extract model metrics
if (metrics==TRUE){
x = fit
n_model = length(x@resp$mu)
n_groups = summary(x)$ngrps
loglik = round(summary(x)$logLik, 2)
aic = round(summary(x)$AICtab[[1]], 1)
metrics.out = paste0(
"Number in model = ", n_model,
", Number of groups = ", paste(n_groups, collapse="/"),
", Log likelihood = ", loglik,
", REML criterion = ", aic)
}
if (metrics==TRUE){
return(list(df.out, metrics.out))
} else {
return(df.out)
}
}
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