lame (version 1.3.4)

loo.ame: Approximate leave-one-out cross-validation for AME / LAME fits

Description

S3 method for loo that uses the per-iteration pointwise log-likelihood stored on the fit object (fit$log_lik) when the model was fit with save_log_lik = TRUE. Returns the standard loo object with Pareto-k diagnostics.

Usage

# S3 method for ame
loo(x, ...)
# S3 method for lame
loo(x, ...)
# S3 method for ame_als
loo(x, ...)

Value

A loo object.

Arguments

x

A fitted ame or lame object that has $log_lik.

...

Additional arguments forwarded to loo::loo.matrix (e.g. cores, r_eff).

Details

What log_lik measures. For family in {normal, binary, cbin, poisson, ordinal} the stored pointwise log-likelihood is the exact family-specific Y density on the response scale, so elpd_loo is directly comparable to a loo() output from Stan / brms fit to the same family. For the rank likelihood frn the exact marginal needs GHK Monte Carlo (Halton sequence); on the longitudinal lame() path you can opt in with log_lik_method = "observed_ghk", on the cross-sectional ame() path the fallback is the augmented-Z normal approximation (with a one-time warning). Inspect fit$log_lik_method on any fit to see which branch was used.

Chunked log-lik portability. When fit with save_log_lik = "chunked", the on-disk chunk files default to tempdir(), which is cleared at the end of the R session. If you intend to saveRDS() the fit and reload it in a fresh session, supply an explicit persistent log_lik_path (e.g. "./loglik_chunks") so the chunks survive the round trip.

Examples

# \donttest{
data(YX_nrm)
fit <- ame(YX_nrm$Y, Xdyad = YX_nrm$X, R = 0,
           nscan = 60, burn = 15, odens = 5,
           save_log_lik = TRUE, verbose = FALSE)
if (requireNamespace("loo", quietly = TRUE)) {
  loo_res <- loo::loo(fit)
  print(loo_res)
}
# }