print_dims

print_dims.importance_sampling

print_dims.psis_loo

print_dims.importance_sampling_loo

print_dims.waic

print_dims.kfold

print_dims.psis_loo_ss

The object returned by <code><a rd-options="=psis" href="/link/psis()?package=loo&version=2.3.1&to=%3Dpsis" data-mini-rdoc="=psis::psis()">psis()</a></code>, <code><a rd-options="=loo" href="/link/loo()?package=loo&version=2.3.1&to=%3Dloo" data-mini-rdoc="=loo::loo()">loo()</a></code>, or <code><a rd-options="=waic" href="/link/waic()?package=loo&version=2.3.1&to=%3Dwaic" data-mini-rdoc="=waic::waic()">waic()</a></code>.

Print dimensions of log-likelihood or log-weights matrix

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

Mans Magnusson

Yuling Yao

Paul-Christian B<c3><bc>rkner

Topi Paananen

Andrew Gelman

Ben Goodrich

Juho Piironen

print_dims function

The object returned by <code><a rd-options='=psis' href='psis()'>psis()</a></code>, <code><a rd-options='=loo' href='loo()'>loo()</a></code>, or <code><a rd-options='=waic' href='waic()'>waic()</a></code>.

Print dimensions of log-likelihood or log-weights matrix — print_dims

Print dimensions of log-likelihood or log-weights matrix

print_dims: Print dimensions of log-likelihood or log-weights matrix

Description

Usage

Arguments