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.
perry(object, ...)the fitted model for which to estimate the prediction error.
additional arguments to be passed down to methods.
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).