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

# \donttest{
model <- suppressWarnings(rstanarm::stan_glm(
  mpg ~ wt + cyl,
  data = mtcars,
  chains = 1,
  iter = 500,
  refresh = 0
))
looic(model)
#> Warning: Found 1 observation(s) with a pareto_k > 0.7. We recommend calling 'loo' again with argument 'k_threshold = 0.7' in order to calculate the ELPD without the assumption that these observations are negligible. This will refit the model 1 times to compute the ELPDs for the problematic observations directly.
#> # LOOIC and ELPD with Standard Error
#> 
#>   LOOIC: 156.75 [9.47]
#>    ELPD: -78.37 [4.73]
# }