| perry {perry} | R Documentation |
Resampling-based prediction error for fitted models
Description
Generic function to estimate the prediction error of a fitted model via
(repeated) K-fold cross-validation, (repeated) random splitting (also
known as random subsampling or Monte Carlo cross-validation), or the
bootstrap.
Usage
perry(object, ...)
Arguments
object |
the fitted model for which to estimate the prediction error. |
... |
additional arguments to be passed down to methods. |
Details
The idea is that developers write easy-to-use methods for end users to
leverage the prediction error estimation framework for their models. A
typical perry method consists of the following two parts: first the
data are extracted from the model, then function perryFit is
called to perform prediction error estimation. The programming effort of
implementing prediction error estimation for a certain model is thus greatly
reduced.
Examples for methods are available in package perryExamples (see
perry-methods).
Author(s)
Andreas Alfons