LOO-related Indices for Bayesian regressions.

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.

Usage

looic(model, verbose = TRUE)

Arguments

model: A Bayesian regression model.
verbose: Toggle off warnings.

Value

A list with four elements, the ELPD, LOOIC and their standard errors.

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]