loo.hsstan

waic.hsstan

waic

Compute an efficient approximate leave-one-out cross-validation
using Pareto smoothed importance sampling (PSIS-LOO), or the widely
applicable information criterion (WAIC), also known as the Watanabe-Akaike
information criterion.

Linear and logistic regression models penalized with hierarchical
shrinkage priors for selection of biomarkers (or more general variable
selection), which can be fitted using Stan (Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
as well as the projection predictive selection approach to recover a sparse
set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
<doi:10.1214/20-EJS1711>).

Marco Colombo

hsstan

Hierarchical Shrinkage Stan Models for Biomarker Selection

Paul McKeigue

Athina Spiliopoulou

loo.hsstan function

<dl><dt>x</dt>
<dd>An object of class <code>hsstan</code>.</dd>
<dt>cores</dt>
<dd>Number of cores used for parallelisation (the value of
<code>options("mc.cores")</code> by default).</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Predictive information criteria for Bayesian models — loo.hsstan

<dl>

<dt>x</dt>
<dd>An object of class <code>hsstan</code>.</dd>


<dt>cores</dt>
<dd>Number of cores used for parallelisation (the value of
<code>options("mc.cores")</code> by default).</dd>


<dt>...</dt>
<dd>Currently ignored.</dd>

</dl>

loo.hsstan: Predictive information criteria for Bayesian models

Description

Usage

Value

Arguments

Examples