Divide indices from 1 to
n into subsets for
k-fold cross validation.
These functions are potentially useful when creating the
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)
Number of data points.
Number of folds.
Random seed so that the same division could be obtained again if needed.
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
the returned value is a list with
each having fields
ts which give the training and test indices, respectively,
for the corresponding fold. If
out='indices', the returned value is a list with fields
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]))