create_model.options

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

create_model.options function

<dl><dt>options</dt>
<dd>A named list with the following possible fields:<ul>
<li><code>delta</code> Amount of added jitter to ensure positive definite
 covariance matrices.</li>
<li><code>vm_params</code> Variance mask function parameters (numeric
 vector of length 2).</li>
</ul>If <code>options</code> is <code>NULL</code>, default options are used. The defaults
are equivalent to
<code>options = list(delta = 1e-8, vm_params = c(0.025, 1))</code>.</dd>
<dt>verbose</dt>
<dd>Should some informative messages be printed?</dd></dl>

Arguments

Parse the given modeling options — create_model.options

<dl>

<dt>options</dt>
<dd>A named list with the following possible fields:<ul>
<li><code>delta</code> Amount of added jitter to ensure positive definite
 covariance matrices.</li>
<li><code>vm_params</code> Variance mask function parameters (numeric
 vector of length 2).</li>
</ul>If <code>options</code> is <code>NULL</code>, default options are used. The defaults
are equivalent to
<code>options = list(delta = 1e-8, vm_params = c(0.025, 1))</code>.</dd>


<dt>verbose</dt>
<dd>Should some informative messages be printed?</dd>

</dl>

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create_model.options: Parse the given modeling options

Description

Usage

Value

Arguments