kfold-generic

kfold

is.kfold

For developers of Bayesian modeling packages, loo includes
a generic function <code>kfold()</code> so that methods may be defined for K-fold
CV without name conflicts between packages. See, for example, the
<code>kfold()</code> methods in the rstanarm and brms packages.
The Value section below describes the objects that <code>kfold()</code>
methods should return in order to be compatible with
<code>loo_compare()</code> and the loo package print methods.

Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as
described in Vehtari, Gelman, and Gabry (2017)
<doi:10.1007/s11222-016-9696-4>.
The approximation uses Pareto smoothed importance sampling (PSIS),
a new procedure for regularizing importance weights.
As a byproduct of the calculations, we also obtain approximate
standard errors for estimated predictive errors and for the comparison
of predictive errors between models. The package also provides methods
for using stacking and other model weighting techniques to average
Bayesian predictive distributions.

Jonah Gabry

Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models

Aki Vehtari

Mans Magnusson

Yuling Yao

Paul-Christian Bürkner

Topi Paananen

Andrew Gelman

Ben Goodrich

Juho Piironen

Bruno Nicenboim

Leevi Lindgren

kfold-generic function

<dl><dt>x</dt>
<dd>A fitted model object.</dd>
<dt>...</dt>
<dd>Arguments to pass to specific methods.</dd></dl>

Arguments

Generic function for K-fold cross-validation for developers — kfold-generic

<dl>

<dt>x</dt>
<dd>A fitted model object.</dd>


<dt>...</dt>
<dd>Arguments to pass to specific methods.</dd>

</dl>

Generic function for K-fold cross-validation for developers

kfold-generic: Generic function for K-fold cross-validation for developers

Description

Usage

Value

Arguments