cvTools (version 0.3.2)

cvFolds: Cross-validation folds

Description

Split \(n\) observations into \(K\) groups to be used for (repeated) \(K\)-fold cross-validation. \(K\) should thereby be chosen such that all groups are of approximately equal size.

Usage

cvFolds(n, K = 5, R = 1,
    type = c("random", "consecutive", "interleaved"))

Value

An object of class "cvFolds" with the following components:

n

an integer giving the number of observations.

K

an integer giving the number of folds.

R

an integer giving the number of replications.

subsets

an integer matrix in which each column contains a permutation of the indices.

which

an integer vector giving the fold for each permuted observation.

Arguments

n

an integer giving the number of observations to be split into groups.

K

an integer giving the number of groups into which the observations should be split (the default is five). Setting K equal to n yields leave-one-out cross-validation.

R

an integer giving the number of replications for repeated \(K\)-fold cross-validation. This is ignored for for leave-one-out cross-validation and other non-random splits of the data.

type

a character string specifying the type of folds to be generated. Possible values are "random" (the default), "consecutive" or "interleaved".

Author

Andreas Alfons

See Also

cvFit, cvSelect, cvTuning

Examples

Run this code
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)

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