relevances

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

relevances function

<dl><dt>fit</dt>
<dd>an object of class <code>lgpfit</code></dd>
<dt>reduce</dt>
<dd>a function to apply to reduce the relevances given each
parameter draw into one value</dd>
<dt>verbose</dt>
<dd>Can this print any messages?</dd>
<dt>...</dt>
<dd>currently has no effect</dd></dl>

Arguments

Assess component relevances — relevances

<dl>

<dt>fit</dt>
<dd>an object of class <code>lgpfit</code></dd>


<dt>reduce</dt>
<dd>a function to apply to reduce the relevances given each
parameter draw into one value</dd>


<dt>verbose</dt>
<dd>Can this print any messages?</dd>


<dt>...</dt>
<dd>currently has no effect</dd>

</dl>

relevances: Assess component relevances

Description

Usage

Value

Arguments