posterior_predict.hsstan

posterior_predict

Draw from the posterior predictive distribution of the outcome.

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_predict.hsstan function

<dl><dt>object</dt>
<dd>An object of class <code>hsstan</code>.</dd>
<dt>newdata</dt>
<dd>Optional data frame containing the variables to use to
predict. If <code>NULL</code> (default), the model matrix is used. If
specified, its continuous variables should be standardized, since
the model coefficients are learnt on standardized data.</dd>
<dt>nsamples</dt>
<dd>A positive integer indicating the number of posterior samples
to use. If <code>NULL</code> (default) all samples are used.</dd>
<dt>seed</dt>
<dd>Optional integer defining the seed for the pseudo-random number
generator.</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Posterior predictive distribution — posterior_predict.hsstan

<dl>

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


<dt>newdata</dt>
<dd>Optional data frame containing the variables to use to
predict. If <code>NULL</code> (default), the model matrix is used. If
specified, its continuous variables should be standardized, since
the model coefficients are learnt on standardized data.</dd>


<dt>nsamples</dt>
<dd>A positive integer indicating the number of posterior samples
to use. If <code>NULL</code> (default) all samples are used.</dd>


<dt>seed</dt>
<dd>Optional integer defining the seed for the pseudo-random number
generator.</dd>


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

</dl>

posterior_predict.hsstan: Posterior predictive distribution

Description

Usage

Value

Arguments

Examples