posterior_crossvalidation: Cross-validation for `graph_lme` models assuming observations at the vertices of metric graphs

Description

This function performs cross-validation by computing predictions for test data using either the posterior distribution from a fitted model (pseudo-CV) or by refitting the model for each fold (true CV).

Usage

posterior_crossvalidation(
  object,
  scores = c("logscore", "crps", "scrps", "mae", "rmse"),
  mode = "k-fold",
  k = 10,
  percentage = 20,
  number_folds = 10,
  train_test_indices = NULL,
  true_CV = FALSE,
  factor = 1,
  tibble = TRUE,
  parallel_folds = FALSE,
  parallel_fitting = FALSE,
  n_cores = parallel::detectCores() - 1,
  print = FALSE,
  seed = NULL,
  return_indices = FALSE,
  use_precomputed = TRUE
)

Value

Vector with the posterior expectations and variances as well as mean absolute error (MAE), root mean squared errors (RMSE), and three negatively oriented proper scoring rules: log-score, CRPS, and scaled CRPS.

Arguments

object: A fitted model using the graph_lme() function or a named list of fitted objects using the graph_lme() function.
scores: A vector of scores to compute. The options are "logscore", "crps", "scrps", "mae", and "rmse". By default, all scores are computed.
mode: Cross-validation mode. Options are "k-fold", "loo" (leave-one-out), or "lpo" (leave-percentage-out). Default is "k-fold".
k: Number of folds for k-fold cross-validation. Default is 10.
percentage: The percentage (from 1 to 99) of the data to be used to train the model. Will only be used if mode is "lpo". Default is 80.
number_folds: Number of folds to be done if mode is "lpo". Default is 10.
train_test_indices: Optional list containing train and test indices for each fold. If provided, k, mode, and percentage are ignored.
true_CV: Logical indicating whether to refit the model for each fold (TRUE) or use the posterior distribution from the fitted model (FALSE). Default is FALSE.
factor: Which factor to multiply the scores. The default is 1.
tibble: Return the scores as a tidyr::tibble()
parallel_folds: Logical indicating whether to run computations in parallel across folds. Default is FALSE.
parallel_fitting: Logical indicating whether to run model fitting in parallel. Default is FALSE.
n_cores: Number of cores to use for parallel computation. Default is parallel::detectCores() - 1.
print: Logical indicating whether to print progress of which fold is being processed. Default is FALSE.
seed: Random seed for reproducibility in fold creation. Default is NULL.
return_indices: Logical indicating whether to return the train/test indices used. Default is FALSE.
use_precomputed: Logical indicating whether to use precomputation for faster CV. Default is TRUE.