add_factor

Easily add a categorical covariate to a data frame

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

add_factor function

<dl><dt>data</dt>
<dd>the original data frame</dd>
<dt>x</dt>
<dd>A named vector containing the category for each individual.
The names should specify the individual id.</dd>
<dt>id_var</dt>
<dd>name of the id variable in <code>data</code></dd></dl>

Arguments

Easily add a categorical covariate to a data frame — add_factor

<dl>

<dt>data</dt>
<dd>the original data frame</dd>


<dt>x</dt>
<dd>A named vector containing the category for each individual.
The names should specify the individual id.</dd>


<dt>id_var</dt>
<dd>name of the id variable in <code>data</code></dd>

</dl>

add_factor: Easily add a categorical covariate to a data frame

Description

Usage

Value

Arguments

See Also