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"))
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.
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".
set.seed(1234) # set seed for reproducibilitycvFolds(20, K = 5, type = "random")
cvFolds(20, K = 5, type = "consecutive")
cvFolds(20, K = 5, type = "interleaved")
cvFolds(20, K = 5, R = 10)