cvFolds | R Documentation |
Split observations or groups of observations into K
folds to be used for (repeated) K
-fold
cross-validation. K
should thereby be chosen such
that all folds are of approximately equal size.
cvFolds(n, K = 5, R = 1,
type = c("random", "consecutive", "interleaved"),
grouping = NULL)
n |
an integer giving the number of observations to
be split into folds. This is ignored if |
K |
an integer giving the number of folds into which
the observations should be split (the default is five).
Setting |
R |
an integer giving the number of replications for
repeated |
type |
a character string specifying the type of
folds to be generated. Possible values are
|
grouping |
a factor specifying groups of observations. If supplied, the data are split according to the groups rather than individual observations such that all observations within a group belong to the same fold. |
An object of class "cvFolds"
with the following
components:
n |
an integer giving the number of observations or groups. |
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 of the observations or groups. |
which |
an integer vector giving the fold for each permuted observation or group. |
grouping |
a list giving the indices of the observations belonging to each group. This is only returned if a grouping factor has been supplied. |
Andreas Alfons
cvFit
, cvSelect
,
cvTuning
set.seed(1234) # set seed for reproducibility
cvFolds(20, K = 5, type = "random")
cvFolds(20, K = 5, type = "consecutive")
cvFolds(20, K = 5, type = "interleaved")
cvFolds(20, K = 5, R = 10)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.