Compute LOOIC (leave-one-out cross-validation (LOO) information criterion) and ELPD (expected log predictive density) for Bayesian regressions. For LOOIC and ELPD, smaller and larger values are respectively indicative of a better fit.
Examples
if (require("rstanarm")) {
model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0)
looic(model)
}
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> # LOOIC and ELPD with Standard Error
#>
#> LOOIC: 155.90 [8.79]
#> ELPD: -77.95 [4.39]