example_fit

Quick way to create an example lgpfit, useful for debugging

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

example_fit function

<dl><dt>formula</dt>
<dd>model formula</dd>
<dt>likelihood</dt>
<dd>observation model</dd>
<dt>chains</dt>
<dd>number of chains to run</dd>
<dt>iter</dt>
<dd>number of iterations to run</dd>
<dt>num_indiv</dt>
<dd>number of individuals (data simulation)</dd>
<dt>num_timepoints</dt>
<dd>number of time points (data simulation)</dd>
<dt>...</dt>
<dd>additional arguments to <code>lgp</code></dd></dl>

Arguments

Quick way to create an example lgpfit, useful for debugging — example_fit

<dl>

<dt>formula</dt>
<dd>model formula</dd>


<dt>likelihood</dt>
<dd>observation model</dd>


<dt>chains</dt>
<dd>number of chains to run</dd>


<dt>iter</dt>
<dd>number of iterations to run</dd>


<dt>num_indiv</dt>
<dd>number of individuals (data simulation)</dd>


<dt>num_timepoints</dt>
<dd>number of time points (data simulation)</dd>


<dt>...</dt>
<dd>additional arguments to <code>lgp</code></dd>

</dl>

example_fit: Quick way to create an example lgpfit, useful for debugging

Description

Usage

Value

Arguments