Every family, and every method within a family, glances to one column set:
nobs, df, logLik, n_fixed, n_samples, n_divergent,
mean_accept and converged, the sampler columns NA on a fit with no
draws. The joint nested-Laplace fits add the outer grid placement and, when
requested, the outer Pareto-k diagnostic. The joint-coupled families
(occu_cover(), cover(), occu() spatial, occu_multiscale_cover())
carry both at the fit top level.
Usage
# S3 method for class 'tobs_fit'
glance(x, ...)Value
The one-row glance data frame. A joint-coupled fit adds two
outer-grid placement columns, reported whether or not the grid moved so an
inert auto-recenter is visible in a batch summary:
outer_grid_placement"fixed"(the grid the fit was given) or"auto_recentered"(the engine moved it onto the hyperparameter mode).outer_grid_recenter_declinedOn a
"fixed"placement, why the recenter did not apply – for instance a user-pinned axis, or"auto_recenter_disabled".NAwhen the grid was recentered.
A fit that also requested the Pareto-k diagnostic (control$diagnose.k = TRUE, off by default) adds three more:
pareto_kThe outer importance-sampling \(\hat{k}\) for the hyperparameter Gaussian summary;
< 0.7indicates a reliable summary,NAwhen the diagnostic did not run or the proposal was degenerate.pareto_k_is_essThe importance-sampling effective sample size on the PSIS-smoothed weights (numeric);
pareto_k_is_ess / control$k.samplesis the relative IS efficiency.pareto_k_proposal_sourceHow the importance proposal was built:
"mode_hessian"from the Laplace curvature at the hyperparameter mode – curvature-backed, so the \(\hat{k}\) stays trustworthy even when a sharp posterior collapses the integration grid to ~1 cell;"grid_moment"from the grid-weighted covariance of the integration nodes (with"moment_matched"its refinement); or"grid_mixture", the local-bump-per-cell mixture matching what the engine samples on a spread grid.