.thin_draws

.thin_draws.matrix

.thin_draws.numeric

.thin_draws.default

internal

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

Måns Magnusson

Yuling Yao

Paul-Christian Bürkner

Topi Paananen

Andrew Gelman

Ben Goodrich

Juho Piironen

Bruno Nicenboim

Leevi Lindgren

.thin_draws function

<dl><dt>draws</dt>
<dd>A draws object with posterior draws.</dd>
<dt>loo_approximation_draws</dt>
<dd>The number of posterior draws to return (ie after thinning).</dd></dl>

Arguments

Thin a draws object — .thin_draws

<dl>

<dt>draws</dt>
<dd>A draws object with posterior draws.</dd>


<dt>loo_approximation_draws</dt>
<dd>The number of posterior draws to return (ie after thinning).</dd>

</dl>

Thin a draws object

.thin_draws: Thin a draws object

Description

Usage

Value

Arguments

Details