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Perform cross-validation on a list of fitted models.

Source: R/inlabru_rspde.R
cross_validation.Rd

Obtain several scores for a list of fitted models according to a folding scheme.

Usage

cross_validation(
  models,
  model_names = NULL,
  scores = c("mae", "mse", "crps", "scrps", "dss"),
  cv_type = c("k-fold", "loo", "lpo"),
  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,
  print = TRUE,
  fit_verbose = FALSE
)

Arguments

models

A fitted model obtained from calling the bru() function or a list of fitted models. All models in the list must have the same number of likelihoods and must be fitted to identical datasets.

model_names

A vector containing the names of the models to appear in the returned data.frame. If NULL, the names will be of the form Model 1, Model 2, and so on. By default, it will try to obtain the name from the models list.

scores

A vector containing the scores to be computed. The options are "mse", "crps", "scrps" and "dss". By default, all scores are computed.

cv_type

The type of the folding to be carried out. The options are k-fold for k-fold cross-validation, in which case the parameter k should be provided, loo, for leave-one-out and lpo for leave-percentage-out, in this case, the parameter percentage should be given, and also the number_folds with the number of folds to be done. The default is k-fold.

k

The number of folds to be used in k-fold cross-validation. Will only be used if cv_type is k-fold.

percentage

The percentage (from 1 to 99) of the data to be used to train the model. Will only be used if cv_type is lpo.

number_folds

Number of folds to be done if cv_type is lpo.

n_samples

Number of samples to compute the posterior statistics to be used to compute the scores.

return_scores_folds

If TRUE, the scores for each fold will also be returned.

orientation_results

character vector. The options are "negative" and "positive". If "negative", the smaller the scores the better. If "positive", the larger the scores the better.

include_best

Should a row indicating which model was the best for each score be included?

train_test_indexes

A list where each element corresponds to a fold. Each fold contains:

  • train: A list of training index vectors, one for each likelihood.

  • test: A list of test index vectors, one for each likelihood, with the same length as train. This list is typically obtained by setting the argument return_train_test to TRUE.

return_train_test

Logical. Should the training and test indexes be returned? If 'TRUE' the train and test indexes will the 'train_test' element of the returned list.

return_post_samples

If TRUE the posterior samples will be included in the returned list.

return_true_test_values

If TRUE the true test values will be included in the returned list.

parallelize_RP

Logical. Should the computation of CRPS and SCRPS (and for some cases, DSS) be parallelized?

n_cores_RP

Number of cores to be used if parallelize_rp is TRUE.

true_CV

Should a TRUE cross-validation be performed? If TRUE the models will be fitted on the training dataset. If FALSE, the parameters will be kept fixed at the ones obtained in the result object.

save_settings

Logical. If TRUE, the settings used in the cross-validation will also be returned.

print

Should partial results be printed throughout the computation?

fit_verbose

Should INLA's run during cross-validation be verbose?

Value

A data.frame with the fitted models and the corresponding scores.