perry: Resampling-based prediction error for fitted models

In aalfons/perry: Resampling-Based Prediction Error Estimation for Regression 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

See Also

perryFit, perry-methods

aalfons/perry documentation built on Nov. 27, 2021, 7:48 a.m.