cross_validation: Perform cross-validation on a list of fitted inlabru models...

In MetricGraph: Random Fields on Metric Graphs

Perform cross-validation on a list of fitted inlabru models on metric graphs.

Description

Mirrors rSPDE::cross_validation() for bru fits (output from inlabru::bru()). For each fold, the held-out responses are set to NA with inlabru::bru_set_missing() and the model is refit with inlabru::bru_rerun(). When true_CV = FALSE, the original fit's mode$theta is held fixed so only the latent posterior is updated; when TRUE, theta is re-estimated. Posterior response samples are then drawn at the held-out locations via inlabru::generate(). Works with the exact, non-FEM SPDE models in MetricGraph (inla_metric_graph_spde) and FEM-based rSPDE models alike. A fit containing an rSPDE component and no exact MetricGraph SPDE is delegated to rSPDE::cross_validation(). Mixed exact/rSPDE model lists use this implementation. Fits without a recognizable package-specific component continue to use the function explicitly called.

Usage

cross_validation(
  models,
  model_names = NULL,
  scores = c("mae", "mse", "crps", "scrps", "dss"),
  cv_type = c("k-fold", "loo", "lpo"),
  weight_thr = NULL,
  k = 5,
  percentage = 20,
  number_folds = 10,
  n_samples = 1000,
  return_scores_folds = FALSE,
  orientation_results = c("negative", "positive"),
  include_best = TRUE,
  train_test_indexes = NULL,
  return_train_test = FALSE,
  return_post_samples = FALSE,
  return_true_test_values = FALSE,
  parallelize_RP = FALSE,
  n_cores_RP = parallel::detectCores() - 1,
  true_CV = TRUE,
  save_settings = FALSE,
  model_options_bru = list(),
  print = TRUE,
  fit_verbose = FALSE
)

Arguments

models	A fitted model from inlabru::bru() or a list of such models. All models must have the same number of likelihoods and be fitted to identical datasets.
model_names	Character vector of model names. Defaults to names(models) or ⁠Model 1⁠, ⁠Model 2⁠, ... if absent.
scores	Subset of c("mae", "mse", "crps", "scrps", "dss", "wcrps", "swcrps").
cv_type	One of "k-fold", "loo", "lpo".
weight_thr	Threshold for wcrps / swcrps. Required if either is requested.
k	Number of folds for k-fold.
percentage	Train percentage (1-99) for lpo.
number_folds	Number of folds for lpo.
n_samples	Number of posterior samples for sample-based scoring.
return_scores_folds	If TRUE, return per-fold score matrices.
orientation_results	One of "negative" (lower is better) or "positive" (higher is better).
include_best	Add a Best row indicating the best model per score.
train_test_indexes	Optional pre-built fold list. If supplied, cv_type, k, percentage, number_folds are ignored.
return_train_test	Return the train/test indices used.
return_post_samples	Return posterior response samples (forces return_scores_folds = TRUE).
return_true_test_values	Return the true response values at test points.
parallelize_RP	Parallelize CRPS/SCRPS pairwise integrals.
n_cores_RP	Number of cores for parallelize_RP.
true_CV	If TRUE, re-estimate hyperparameters on each training fold; if FALSE, update the latent posterior on the training fold with the original hyperparameters fixed.
save_settings	If TRUE, return the CV settings used.
model_options_bru	List of options passed to inlabru.
print	Print partial progress.
fit_verbose	Pass verbose through to INLA when refitting.

Value

Either a data.frame (default), or a list with scores_df plus the optional components requested by ⁠return_*⁠ / save_settings arguments.

MetricGraph documentation built on Nov. 11, 2026, 1:09 a.m.