These functions represent different cross-validation schemes that can be
used with origami. They should be used as options for the
`fold_fun`

argument to `make_folds`

, which will call the
requested function specify `n`

, based on its arguments, and pass any
remaining arguments (e.g. `V`

or `pvalidation`

) on.

folds_vfold(n, V = 10L) folds_resubstitution(n) folds_loo(n) folds_montecarlo(n, V = 1000L, pvalidation = 0.2) folds_bootstrap(n, V = 1000L) folds_rolling_origin(n, first_window, validation_size, gap = 0L, batch = 1L) folds_rolling_window(n, window_size, validation_size, gap = 0L, batch = 1L) folds_rolling_origin_pooled( n, t, id = NULL, time = NULL, first_window, validation_size, gap = 0L, batch = 1L ) folds_rolling_window_pooled( n, t, id = NULL, time = NULL, window_size, validation_size, gap = 0L, batch = 1L ) folds_vfold_rolling_origin_pooled( n, t, id = NULL, time = NULL, V = 10L, first_window, validation_size, gap = 0L, batch = 1L ) folds_vfold_rolling_window_pooled( n, t, id = NULL, time = NULL, V = 10L, window_size, validation_size, gap = 0L, batch = 1L )

n | An integer indicating the number of observations. |
---|---|

V | An integer indicating the number of folds. |

pvalidation | A |

first_window | An integer indicating the number of observations in the first training sample. |

validation_size | An integer indicating the number of points in the validation samples; should be equal to the largest forecast horizon. |

gap | An integer indicating the number of points not included in the training or validation samples. The default is zero. |

batch | An integer indicating increases in the number of time points added to the training set in each iteration of cross-validation. Applicable for larger time-series. The default is one. |

window_size | An integer indicating the number of observations in each training sample. |

t | An integer indicating the total amount of time to consider per time-series sample. |

id | An optional vector of unique identifiers corresponding to the time vector. These can be used to subset the time vector. |

time | An optional vector of integers of time points observed for each subject in the sample. |

A list of `Fold`

s.

Other fold generation functions:
`fold_from_foldvec()`

,
`folds2foldvec()`

,
`make_folds()`

,
`make_repeated_folds()`