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Compute LOO-CV

Source: R/fitter.R
loo_fit.Rd

Compute leave-one-out cross-validation using Pareto-smoothed importance sampling (PSIS-LOO). Named loo_fit to avoid clashing with loo::loo().

Usage

loo_fit(fitter, fit_result, log_lik = NULL)

Arguments

fitter

An S7 Fitter object

fit_result

A bayesim_fit_result object from fit_model()

log_lik

Optional pointwise log-likelihood matrix (S x N, draws x observations) for the training set, as returned by log_lik_matrix(). When supplied, methods should use it instead of recomputing their own; build_loo_context() passes the matrix it already computed so the weighted-prediction (PSIS) path pays for it once per task (#73). NULL (the default, and for standalone calls) means the method computes its own.

Value

A list containing:

  • elpd: Expected log predictive density (scalar)

  • p_loo: Effective number of parameters (scalar)

  • elpd_se: Standard error of ELPD (scalar)

  • pareto_k: Pareto k diagnostic values (vector of length N)

  • r_eff: Chain-aware relative efficiencies used for the summary (vector of length N), or NULL when none were computed (e.g. i.i.d. draws). build_loo_context() reuses it for the PSIS object (#73).

  • Additional loo-specific diagnostics