fit.submodel

internal

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

fit.submodel function

<dl><dt>x</dt>
<dd>Design matrix.</dd>
<dt>sigma2</dt>
<dd>Residual variance (1 for logistic regression).</dd>
<dt>mu</dt>
<dd>Matrix of fitted values for the full model.</dd>
<dt>chosen</dt>
<dd>Vector of indices of the columns of <code>x</code> in the current submodel.</dd>
<dt>xt</dt>
<dd>Design matrix for test data.</dd>
<dt>yt</dt>
<dd>Outcome variable for test data.</dd>
<dt>logistic</dt>
<dd>Set to <code>TRUE</code> for a logistic regression model.</dd></dl>

Arguments

Compute the fit of a submodel — fit.submodel

<dl>

<dt>x</dt>
<dd>Design matrix.</dd>


<dt>sigma2</dt>
<dd>Residual variance (1 for logistic regression).</dd>


<dt>mu</dt>
<dd>Matrix of fitted values for the full model.</dd>


<dt>chosen</dt>
<dd>Vector of indices of the columns of <code>x</code> in the current submodel.</dd>


<dt>xt</dt>
<dd>Design matrix for test data.</dd>


<dt>yt</dt>
<dd>Outcome variable for test data.</dd>


<dt>logistic</dt>
<dd>Set to <code>TRUE</code> for a logistic regression model.</dd>

</dl>

fit.submodel: Compute the fit of a submodel

Description

Usage

Arguments