lm_proj

Compute projections of full predictors on to subspace of predictors

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

lm_proj function

<dl><dt>x</dt>
<dd>Design matrix.</dd>
<dt>fit</dt>
<dd>Matrix of fitted values for the full model.</dd>
<dt>sigma2</dt>
<dd>Residual variance (1 for logistic regression).</dd>
<dt>indproj</dt>
<dd>Vector of indices of the columns of <code>x</code> that form the
projection subspace.</dd>
<dt>is.logistic</dt>
<dd>Set to <code>TRUE</code> for a logistic regression model.</dd></dl>

Arguments

Compute projections of full predictors on to subspace of predictors — lm_proj

<dl>

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


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


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


<dt>indproj</dt>
<dd>Vector of indices of the columns of <code>x</code> that form the
projection subspace.</dd>


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

</dl>

lm_proj: Compute projections of full predictors on to subspace of predictors

Description

Usage

Arguments