posterior_interval.hsstan

posterior_interval

Compute posterior uncertainty intervals for <code>hsstan</code> objects.

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

posterior_interval.hsstan function

<dl><dt>object</dt>
<dd>An object of class <code>hsstan</code>.</dd>
<dt>pars</dt>
<dd>Names of parameters for which posterior intervals should be
returned, which can be specified as regular expressions. If <code>NULL</code>
(default) then this refers to the set of predictors used in the model.</dd>
<dt>prob</dt>
<dd>A value between 0 and 1 indicating the desired probability
to be covered by the uncertainty intervals (0.95, by default).</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Posterior uncertainty intervals — posterior_interval.hsstan

<dl>

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


<dt>pars</dt>
<dd>Names of parameters for which posterior intervals should be
returned, which can be specified as regular expressions. If <code>NULL</code>
(default) then this refers to the set of predictors used in the model.</dd>


<dt>prob</dt>
<dd>A value between 0 and 1 indicating the desired probability
to be covered by the uncertainty intervals (0.95, by default).</dd>


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

</dl>

posterior_interval.hsstan: Posterior uncertainty intervals

Description

Usage

Value

Arguments

Examples