splitTools (version 1.0.1)

create_folds: Create Folds

Description

This function provides a list of row indices used for k-fold cross-validation (basic, stratified, grouped, or blocked). Repeated fold creation is supported as well. By default, in-sample indices are returned.

Usage

create_folds(
  y,
  k = 5L,
  type = c("stratified", "basic", "grouped", "blocked"),
  n_bins = 10L,
  m_rep = 1L,
  use_names = TRUE,
  invert = FALSE,
  shuffle = FALSE,
  seed = NULL
)

Value

If invert = FALSE (the default), a list with in-sample row indices. If invert = TRUE, a list with out-of-sample indices.

Arguments

y

Either the variable used for "stratification" or "grouped" splits. For other types of splits, any vector of the same length as the data intended to split.

k

Number of folds.

type

Split type. One of "stratified" (default), "basic", "grouped", "blocked".

n_bins

Approximate numbers of bins for numeric y (only for type = "stratified").

m_rep

How many times should the data be split into k folds? Default is 1, i.e., no repetitions.

use_names

Should folds be named? Default is TRUE.

invert

Set to TRUE in order to receive out-of-sample indices. Default is FALSE, i.e., in-sample indices are returned.

shuffle

Should row indices be randomly shuffled within folds? Default is FALSE.

seed

Integer random seed.

Details

By default, the function uses stratified splitting. This will balance the folds regarding the distribution of the input vector y. (Numeric input is first binned into n_bins quantile groups.) If type = "grouped", groups specified by y are kept together when splitting. This is relevant for clustered or panel data. In contrast to basic splitting, type = "blocked" does not sample indices at random, but rather keeps them in sequential groups.

See Also

partition(), create_timefolds()

Examples

y <- rep(c(letters[1:4]), each = 5)
create_folds(y)
create_folds(y, k = 2)
create_folds(y, k = 2, m_rep = 2)
create_folds(y, k = 3, type = "blocked")