get_pred

NOTE: It is not recommended for users to call this. Use
<code>pred</code> instead.

Interpretable nonparametric modeling of longitudinal data
using additive Gaussian process regression. Contains functionality
for inferring covariate effects and assessing covariate relevances.
Models are specified using a convenient formula syntax, and can include
shared, group-specific, non-stationary, heterogeneous and temporally
uncertain effects. Bayesian inference for model parameters is performed
using 'Stan'. The modeling approach and methods are described in detail in
Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Juho Timonen

lgpr

Longitudinal Gaussian Process Regression

Andrew Johnson

get_pred function

<dl><dt>fit</dt>
<dd>An object of class lgpfit.</dd>
<dt>draws</dt>
<dd>Indices of parameter draws to use, or <code>NULL</code> to use all
draws.</dd>
<dt>reduce</dt>
<dd>Reduction for parameters draws. Can be a function that
is applied to reduce all parameter draws into one parameter set, or
<code>NULL</code> (no reduction). Has no effect if <code>draws</code> is specified.</dd>
<dt>verbose</dt>
<dd>Should more information and a possible progress bar be
printed?</dd></dl>

Arguments

Extract model predictions and function posteriors — get_pred

<dl>

<dt>fit</dt>
<dd>An object of class lgpfit.</dd>


<dt>draws</dt>
<dd>Indices of parameter draws to use, or <code>NULL</code> to use all
draws.</dd>


<dt>reduce</dt>
<dd>Reduction for parameters draws. Can be a function that
is applied to reduce all parameter draws into one parameter set, or
<code>NULL</code> (no reduction). Has no effect if <code>draws</code> is specified.</dd>


<dt>verbose</dt>
<dd>Should more information and a possible progress bar be
printed?</dd>

</dl>

get_pred: Extract model predictions and function posteriors

Description

Usage

Value

Arguments