Monte-Carlo cross-validation strategy

Monte-Carlo cross-validation strategy

Usage

mc(k, split)

Arguments

k

Integer. Number of repeated random splits.

split

Numeric. Fraction of samples assigned to the training set (e.g. 2/3).

Value

A named list with elements:

train

List of integer vectors containing training set indices for each resampling iteration.

strategy

Character string indicating the resampling strategy.

n

Integer. Number of samples in the dataset.

seed

Integer. Random seed used to generate the resampling splits, ensuring reproducibility.

Details

Monte-Carlo cross-validation generates k random train/test splits without replacement. No stratification is applied; samples are drawn uniformly at random.

See also

Other resampling strategies: balanced_boot(), balanced_mc(), kfold(), stratified_kfold()

Examples

n <- 100
# bivariate outcome
thr <- 1.5
Y <- c(rnorm(80, thr-3, 0.3), rnorm(20, thr+3, 0.3))  # unbalanced low/high outcome
mean(Y>thr)
#> [1] 0.2

cv_mc <- mc(k = 10, split = 2/3)
mc_inst <- metabom8:::.arg_check_cv(cv_pars=cv_mc, model_type='R', n=n, Y_prepped=cbind(Y))
sapply(mc_inst$train, function(i) length(i))
#>  [1] 66 66 66 66 66 66 66 66 66 66