log_lik.hsstan

log_lik

Compute the pointwise log-likelihood.

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

log_lik.hsstan function

<dl><dt>object</dt>
<dd>An object of class <code>hsstan</code>.</dd>
<dt>newdata</dt>
<dd>Optional data frame to use to evaluate the log-likelihood.
If <code>NULL</code> (default), the model matrix is used.</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Pointwise log-likelihood — log_lik.hsstan

<dl>

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


<dt>newdata</dt>
<dd>Optional data frame to use to evaluate the log-likelihood.
If <code>NULL</code> (default), the model matrix is used.</dd>


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

</dl>

log_lik.hsstan: Pointwise log-likelihood

Description

Usage

Value

Arguments

Examples