Rstanarm: extracting variance components

Interfaces rstanarm
4

Outside of a Gaussian, linear, no link function model, the idea of a decomposition of variance does not make sense. The general, correct, and Bayesian way to do things is

PPD <- posterior_predict(post)
vars <- apply(PPD, MARGIN = 1, FUN = var)

PPD_0 <- posterior_predict(post, re.form = ~ 0)
vars_0 <- apply(PPD_0, MARGIN = 1, FUN = var)

summary(vars - vars_0)

In general, it is better to just show a plot of the entire margin of the posterior distribution that is of interest, but if you are going to focus on a number it should usually be the mean or median. The posterior mode is not useful.