These functions are provided for compatibility with older versions only, and may be defunct as soon as the next release.
repCV(object, K = 5, R = 1,
foldType = c("random", "consecutive", "interleaved"),
grouping = NULL, folds = NULL, ...) repRS(object, m, R = 1, grouping = NULL, splits = NULL,
...)
bootPE(object, R = 1,
bootType = c("0.632", "out-of-bag"), grouping = NULL,
samples = NULL, ...)
the fitted model for which to estimate the prediction error.
an integer giving the number of folds into which
the observations should be split (the default is five).
Setting K equal to the number of observations or
groups yields leave-one-out cross-validation.
an integer giving the number of observations or groups of observations to be used as test data.
an integer giving the number of replications.
In repCV, this is ignored for for leave-one-out
cross-validation and other non-random splits of the
data.
a character string specifying the type of
folds to be generated. Possible values are
"random" (the default), "consecutive" or
"interleaved".
a character string specifying a bootstrap
estimator. Possible values are "0.632" (the
default), or "out-of-bag".
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 either to the training or test data.
an object of class "cvFolds" (as
returned by cvFolds) or a control object of
class "foldControl" (see
foldControl) defining the folds of the data
for (repeated) \(K\)-fold cross-validation. If
supplied, this is preferred over the arguments for
generating cross-validation folds.
an object of class "randomSplits"
(as returned by randomSplits) or a control
object of class "splitControl" (see
splitControl) defining random data splits.
If supplied, this is preferred over the arguments for
generating random data splits.
an object of class "bootSamples"
(as returned by bootSamples) or a control
object of class "bootControl" (see
bootControl) defining bootstrap samples.
If supplied, this is preferred over the arguments for
generating bootstrap samples.
additional arguments to be passed down to
perry.
repCV, repRS and bootPE are wrapper
functions for perry that perform (repeated)
\(K\)-fold cross-validation, (repeated) random
splitting (also known as random subsampling or Monte
Carlo cross-validation) and the bootstrap, respectively.