ForestSearch Repeated K-Fold Cross-Validation

This function performs multiple independent K-fold cross-validations to assess the variability in subgroup identification. Each simulation:

Randomly shuffles the data
Performs K-fold CV
Records sensitivity and agreement metrics

Results are summarized across all simulations.

Usage

forestsearch_tenfold(
  fs.est,
  sims,
  Kfolds = 10,
  details = TRUE,
  seed = 8316951L,
  parallel_args = list(plan = "multisession", workers = 6, show_message = TRUE)
)

Arguments

fs.est: List. ForestSearch results object from forestsearch.
sims: Integer. Number of simulation repetitions.
Kfolds: Integer. Number of folds per simulation (default: 10).
details: Logical. Print progress details (default: TRUE).
seed: Integer. Base random seed for fold shuffling. Default 8316951L. Each simulation uses seed + 1000 * ksim for reproducibility.
parallel_args: List. Parallelization configuration.

Value

List with components:

sens_summary: Named vector of median sensitivity metrics across simulations
find_summary: Named vector of median subgroup-finding metrics
sens_out: Matrix of sensitivity metrics (sims x metrics)
find_out: Matrix of finding metrics (sims x metrics)
timing_minutes: Total execution time
sims: Number of simulations run
Kfolds: Number of folds per simulation

Details

Runs repeated K-fold cross-validation simulations for ForestSearch and summarizes subgroup identification stability across repetitions.

Parallelization Strategy

Unlike the single K-fold function which parallelizes across folds, this function parallelizes across simulations for better efficiency when running many repetitions. Each simulation runs its K-fold CV sequentially.

Examples

if (FALSE) { # \dontrun{
# Run 100 repetitions of 10-fold CV
tenfold_results <- forestsearch_tenfold(
  fs.est = fs_result,
  sims = 100,
  Kfolds = 10,
  parallel_args = list(plan = "multisession", workers = 6),
  details = TRUE
)

# View summary
print(tenfold_results$sens_summary)
print(tenfold_results$find_summary)
} # }