Arguments
k
integer, specify $k$ for $k$-fold cross-validation.
nboot
integer, number of bootstrap replications.
strat
logical, if TRUE, cross-validation is performed
using stratified sampling (for classification problems).
random
logical, if TRUE, cross-validation is performed using
a random ordering of the data.
predictions
logical, indicates whether the prediction
for each observation should be returned or not
(classification and regression only). For a bootstrap
based estimator a matrix of size 'number of observations' times nboot is
returned with predicted values of the ith out-of-bootstrap sample in column
i and 'NA's for those observations not included in the ith out-of-bootstrap
sample.
getmodels
logical, indicates a list of all models should be
returned. For cross-validation only.
list.tindx
list of numeric vectors, indicating which
observations are included in each bootstrap or cross-validation sample, respectively.