% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stats_cv_sets_methods.R
\name{mc}
\alias{mc}
\title{Monte-Carlo cross-validation strategy}
\usage{
mc(k, split)
}
\arguments{
\item{k}{Integer. Number of repeated random splits.}

\item{split}{Numeric. Fraction of samples assigned to the
training set (e.g. \code{2/3}).}
}
\value{
A named \code{list} with elements:
\describe{
\item{train}{List of integer vectors containing training set indices
for each resampling iteration.}
\item{strategy}{Character string indicating the resampling strategy.}
\item{n}{Integer. Number of samples in the dataset.}
\item{seed}{Integer. Random seed used to generate the resampling splits,
ensuring reproducibility.}
}
}
\description{
Monte-Carlo cross-validation strategy
}
\details{
Monte-Carlo cross-validation generates \code{k} random train/test splits
without replacement. No stratification is applied; samples are drawn
uniformly at random.
}
\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)

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))
}
\seealso{
Other resampling strategies: 
\code{\link{balanced_boot}()},
\code{\link{balanced_mc}()},
\code{\link{kfold}()},
\code{\link{stratified_kfold}()}
}
\concept{resampling strategies}
