R: Cross-validation folds

cvFolds {cvTools} | R Documentation

## 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"))

### 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"`.

### 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.

### Author(s)

Andreas Alfons

### See Also

`cvFit`, `cvSelect`, `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)

[Package cvTools version 0.3.2 Index]