get_crossvalidated_model_fit: Get cross-validated model fit

In XSLmodels: Run and Optimize Cross-Situational Word Learning Models

Get cross-validated model fit

Description

Performs k-fold cross-validation on a model using the available datasets: the datasets are split into n_folds groups, and for each fold the model is fit (via xsl_fit()) to the other folds and evaluated (via xsl_run()) on the held-out fold.

Usage

get_crossvalidated_model_fit(
  model_name,
  n_folds = 5,
  datasets = NULL,
  control = xslControl(),
  deoptim_control = DEoptim::DEoptim.control(reltol = 0.001, NP = 100, itermax = 100)
)

Arguments

model_name	Name of the model to cross-validate (see show_models())
n_folds	Number of folds for cross-validation (default: 5)
datasets	Optional list of datasets to use (defaults to all available)
control	Control arguments passed to xsl_run()/xsl_fit(); notably n_sim, the number of simulations averaged per evaluation of a stochastic model (default 500 – lowering this can speed up cross-validation of stochastic models substantially).
deoptim_control	Control parameters passed to DEoptim() (via cross_validated_group_fits()).

Value

A list containing cross-validation results (see cross_validated_group_fits())

Examples

# Get cross-validated fit for the decay model. A reduced DEoptim search
# keeps this example fast; drop deoptim_control for a real, thorough fit.
cv_fit <- get_crossvalidated_model_fit(
  "decay", n_folds = 2, datasets = xsl_datasets[1:6],
  deoptim_control = DEoptim::DEoptim.control(NP = 10, itermax = 5))

XSLmodels documentation built on Oct. 18, 2026, 5:08 p.m.