cv-indices | R Documentation |

These are helper functions to create cross-validation (CV) folds, i.e., to
split up the indices from 1 to `n`

into `K`

subsets ("folds") for
*K*-fold CV. These functions are potentially useful when creating the
`cvfits`

and `cvfun`

arguments for `init_refmodel()`

. The return value is
different for these two methods, see below for details.

cvfolds(n, K, seed = sample.int(.Machine$integer.max, 1)) cv_ids( n, K, out = c("foldwise", "indices"), seed = sample.int(.Machine$integer.max, 1) )

`n` |
Number of observations. |

`K` |
Number of folds. Must be at least 2 and not exceed |

`seed` |
Pseudorandom number generation (PRNG) seed by which the same
results can be obtained again if needed. Passed to argument |

`out` |
Format of the output, either |

`cvfolds()`

returns a vector of length `n`

such that each element is
an integer between 1 and `K`

denoting which fold the corresponding data
point belongs to. The return value of `cv_ids()`

depends on the `out`

argument. If `out = "foldwise"`

, the return value is a `list`

with `K`

elements, each being a `list`

with elements `tr`

and `ts`

giving the
training and test indices, respectively, for the corresponding fold. If
`out = "indices"`

, the return value is a `list`

with elements `tr`

and `ts`

each being a `list`

with `K`

elements giving the training and test indices,
respectively, for each fold.

n <- 100 set.seed(1234) y <- rnorm(n) cv <- cv_ids(n, K = 5, seed = 9876) # Mean within the test set of each fold: cvmeans <- sapply(cv, function(fold) mean(y[fold$ts]))

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