Expected log predictive density from cross validation for loo

Converts the pointwise out-of-sample log predictive densities (cv_loglik) from sdmTMB_cv() into a loo elpd_generic object. This lets loo::loo_compare() compare models, including a standard error of the difference in expected log predictive density.

Usage

# S3 method for class 'sdmTMB_cv'
elpd(x, ...)

Arguments

x

Output from sdmTMB_cv().

...

Not used.

Value

An object of class elpd_generic and loo. See loo::elpd().

Details

Models compared with loo::loo_compare() must be fit to the same data with the same folds (e.g., via fold_ids) and should use the same predictive argument. loo only checks that the number of observations matches. The standard errors reflect variation across observations, not the Monte Carlo error from nsim.

The printed object refers to a "1 by N log-likelihood matrix" because the pointwise values are already integrated over draws within sdmTMB_cv().

See also

sdmTMB_cv(), compare_deviance() for in-sample deviance explained.

Examples

# \donttest{
if (requireNamespace("loo", quietly = TRUE)) {
  mesh <- make_mesh(pcod_2011, c("X", "Y"), cutoff = 20)
  folds <- rep(1:4, length.out = nrow(pcod_2011))
  m1 <- sdmTMB_cv(density ~ 1, data = pcod_2011, mesh = mesh,
    family = tweedie(), fold_ids = folds)
  m2 <- sdmTMB_cv(density ~ depth_scaled + depth_scaled2,
    data = pcod_2011, mesh = mesh, family = tweedie(), fold_ids = folds)
  loo::loo_compare(list(null = loo::elpd(m1), depth = loo::elpd(m2)))
}
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
#>  model elpd_diff se_diff p_worse diag_diff diag_elpd
#>  depth       0.0     0.0      NA                    
#>   null     -73.0    15.1    1.00                    
# }