`cv-indices.Rd`

Divide indices from 1 to `n`

into subsets for `k`

-fold cross validation.
These functions are potentially useful when creating the `cvfits`

and `cvfun`

arguments for init_refmodel. The returned value is different for
these two methods, see below for details.

cvfolds(n, k, seed = NULL) cvind(n, k, out = "foldwise", seed = NULL)

n | Number of data points. |
---|---|

k | Number of folds. |

seed | Random seed so that the same division could be obtained again if needed. |

out | Format of the output, either 'foldwise' (default) or 'indices'. See below for details. |

`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 returned value of `cvind`

depends on the `out`

-argument. If `out`

='foldwise',
the returned value is a list with `k`

elements,
each having fields `tr`

and `ts`

which give the training and test indices, respectively,
for the corresponding fold. If `out`

='indices', the returned value is a list with fields `tr`

and `ts`

each of which is a list with `k`

elements giving the training and test indices for each fold.

### compute sample means within each fold n <- 100 y <- rnorm(n) cv <- cvind(n, k=5) cvmeans <- lapply(cv, function(fold) mean(y[fold$tr]))