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```
# ----------------------
# Author: Andreas Alfons
# KU Leuven
# ----------------------
## split data into blocks for cross-validation as in package 'lars'
#' Cross-validation folds
#'
#' Split observations or groups of observations into \eqn{K} folds to be used
#' for (repeated) \eqn{K}-fold cross-validation. \eqn{K} should thereby be
#' chosen such that all folds are of approximately equal size.
#'
#' @aliases print.cvFolds
#'
#' @param n an integer giving the number of observations to be split into
#' folds. This is ignored if \code{grouping} is supplied in order to split
#' groups of observations into folds.
#' @param K an integer giving the number of folds into which the observations
#' should be split (the default is five). Setting \code{K} equal to the number
#' of observations or groups yields leave-one-out cross-validation.
#' @param R an integer giving the number of replications for repeated
#' \eqn{K}-fold cross-validation. This is ignored for for leave-one-out
#' cross-validation and other non-random splits of the data.
#' @param type a character string specifying the type of folds to be
#' generated. Possible values are \code{"random"} (the default),
#' \code{"consecutive"} or \code{"interleaved"}.
#' @param grouping a factor specifying groups of observations. If supplied,
#' the data are split according to the groups rather than individual
#' observations such that all observations within a group belong to the same
#' fold.
#'
#' @return An object of class \code{"cvFolds"} with the following components:
#' \item{n}{an integer giving the number of observations or groups.}
#' \item{K}{an integer giving the number of folds.}
#' \item{R}{an integer giving the number of replications.}
#' \item{subsets}{an integer matrix in which each column contains a
#' permutation of the indices of the observations or groups.}
#' \item{which}{an integer vector giving the fold for each permuted
#' observation or group.}
#' \item{grouping}{a list giving the indices of the observations
#' belonging to each group. This is only returned if a grouping factor
#' has been supplied.}
#'
#' @author Andreas Alfons
#'
#' @seealso \code{\link{cvFit}}, \code{\link{cvSelect}}, \code{\link{cvTuning}}
#'
#' @examples
#' set.seed(1234) # set seed for reproducibility
#' cvFolds(20, K = 5, type = "random")
#' cvFolds(20, K = 5, type = "consecutive")
#' cvFolds(20, K = 5, type = "interleaved")
#' cvFolds(20, K = 5, R = 10)
#'
#' @keywords utilities
#'
#' @export
cvFolds <- function(n, K = 5, R = 1,
type = c("random", "consecutive", "interleaved"),
grouping = NULL) {
# check arguments
if(is.null(grouping)) {
n <- round(rep(n, length.out=1))
} else {
grouping <- as.factor(grouping)
n <- nlevels(grouping)
}
if(!isTRUE(n > 0)) stop("'n' must be positive")
K <- round(rep(K, length.out=1))
if(!isTRUE((K > 1) && K <= n)) stop("'K' outside allowable range")
type <- if(K == n) "leave-one-out" else match.arg(type)
# obtain CV folds
if(type == "random") {
# random K-fold splits with R replications
R <- round(rep(R, length.out=1))
if(!isTRUE(R > 0)) R <- 1
subsets <- replicate(R, sample(n))
} else {
# leave-one-out CV or non-random splits, replication not meaningful
R <- 1
subsets <- as.matrix(seq_len(n))
}
which <- rep(seq_len(K), length.out=n)
if(type == "consecutive") which <- rep.int(seq_len(K), tabulate(which))
# construct and return object
folds <- list(n=n, K=K, R=R, subsets=subsets, which=which)
if(!is.null(grouping)) {
folds$grouping <- split(seq_along(grouping), grouping)
}
class(folds) <- "cvFolds"
folds
}
## retrieve CV folds for r-th replication
getSubsetList <- function(x, ...) UseMethod("getSubsetList")
getSubsetList.cvFolds <- function(x, r = 1, ...) {
# split permuted items according to the folds
subsetList <- split(x$subsets[, r], x$which)
# in case of grouped data, the list contains the group indices in each CV
# fold, so the indices of the respective observations need to be extracted
if(!is.null(grouping <- x$grouping)) {
subsetList <- lapply(subsetList,
function(s) {
unlist(grouping[s], use.names=FALSE)
})
}
# return list of indices for CV folds
subsetList
}
```

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