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Leave-one-out pseudo-crossvalidation for graph_lme models assuming observations at the vertices of metric graphs

Source: R/graph_inference.R
posterior_crossvalidation.Rd

This function performs pseudo-crossvalidation by computing leave-one-out predictions using the posterior distribution from a fitted model. In pseudo-crossvalidation, the model parameters are kept fixed at the values estimated from the full dataset (those provided in the object), rather than re-estimating them for each fold.

Usage

posterior_crossvalidation(
  object,
  factor = 1,
  tibble = TRUE,
  parallel = FALSE,
  n_cores = parallel::detectCores() - 1,
  print = FALSE
)

Arguments

object

A fitted model using the graph_lme() function or a named list of fitted objects using the graph_lme() function.

factor

Which factor to multiply the scores. The default is 1.

tibble

Return the scores as a tidyr::tibble()

parallel

Logical indicating whether to run computations 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.

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.