loo_and_waic

loo_and_waic_diff

an $S$ by $N$ matrix, where $S$ is the size of the
posterior sample (the number of simulations) and $N$ is the number of
data points. Typically (but not restricted to be) the object returned by
<code><a href="/link/extract_log_lik?package=loo&version=0.1.2" rd-options="" data-mini-rdoc="loo::extract_log_lik">extract_log_lik</a></code>

log_lik

optional arguments to pass to <code><a href="/link/psislw?package=loo&version=0.1.2" rd-options="" data-mini-rdoc="loo::psislw">psislw</a></code>. Possible
arguments and their defaults are:
[object Object],[object Object],[object Object],[object Object],[object Object]<p></p><p>We recommend using the default values for the</p>

This function is deprecated. Please use <code><a href="/link/loo?package=loo&version=0.1.2" rd-options="" data-mini-rdoc="loo::loo">loo</a></code> or
<code><a href="/link/waic?package=loo&version=0.1.2" rd-options="" data-mini-rdoc="loo::waic">waic</a></code> instead.

internal

We efficiently approximate leave-one-out cross-validation (LOO)
  using Pareto smoothed importance sampling (PSIS), a new procedure for
  regularizing importance weights. As a byproduct of our calculations, we also
  obtain approximate standard errors for estimated predictive errors, and for
  the comparison of predictive errors between two models. We also compute the
  widely applicable information criterion (WAIC).

loo_and_waic: Approximate LOO-CV and WAIC for Bayesian models

Description

Usage

Arguments

Value