Some parameters that control the behaviour of errorest.
control.errorest(k = 10, nboot = 25, strat = FALSE, random = TRUE,
predictions = FALSE, getmodels=FALSE, list.tindx = NULL)integer, specify $k$ for $k$-fold cross-validation.
integer, number of bootstrap replications.
logical, if TRUE, cross-validation is performed
using stratified sampling (for classification problems).
logical, if TRUE, cross-validation is performed using
a random ordering of the data.
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.
logical, indicates a list of all models should be returned. For cross-validation only.
list of numeric vectors, indicating which observations are included in each bootstrap or cross-validation sample, respectively.
A list with the same components as arguments.