psislw

A matrix or vector of log weights. For computing LOO <code>lw =
-log_lik</code> (see <code><a href="/link/extract_log_lik?package=loo&version=0.1.3" rd-options="" data-mini-rdoc="loo::extract_log_lik">extract_log_lik</a></code>) and is an $S$ by $N$
matrix where $S$ is the number of simulations and $N$ is the number
of data

The proportion of importance weights to use for the generalized
Pareto fit. The <code>100*wcp</code>% largest weights are used as the sample
from which to estimate the parameters of the generalized Pareto
distribution.

For truncating very large weights to $S$^<code>wtrunc</code>. Set
to zero for no truncation.

wtrunc

The number of cores to use for parallelization. This can be set
for an entire R session by <code>options(loo.cores = NUMBER)</code>. The default is
<code><a href="/link/detectCores?package=loo&version=0.1.3&to=parallel" rd-options="parallel" data-mini-rdoc="parallel::detectCores">detectCores</a></code>().

cores

See <code><a href="/link/loo.function?package=loo&version=0.1.3" rd-options="" data-mini-rdoc="loo::loo.function">loo.function</a></code>.

llfun,llargs

Ignored when <code>psislw</code> is called directly. The <code>...</code> is
  only used internally when <code>psislw</code> is called by the <code><a href="/link/loo?package=loo&version=0.1.3" rd-options="" data-mini-rdoc="loo::loo">loo</a></code>
  function.

Pareto smoothed importance sampling (PSIS)

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).

psislw: Pareto smoothed importance sampling (PSIS)

Description

Usage

Arguments

Value

Details

See Also