add_dis_age

Creates the disease-related age covariate vector based on the
disease initiation times and adds it to the 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_dis_age function

<dl><dt>data</dt>
<dd>the original data frame</dd>
<dt>t_init</dt>
<dd>A named vector containing the observed initiation or onset
time for each individual. The names, i.e. <code>names(t_init)</code>, should
specify the individual id.</dd>
<dt>id_var</dt>
<dd>name of the id variable in <code>data</code></dd>
<dt>time_var</dt>
<dd>name of the time variable in <code>data</code></dd></dl>

Arguments

Easily add the disease-related age variable to a data frame — add_dis_age

<dl>

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


<dt>t_init</dt>
<dd>A named vector containing the observed initiation or onset
time for each individual. The names, i.e. <code>names(t_init)</code>, should
specify the individual id.</dd>


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


<dt>time_var</dt>
<dd>name of the time variable in <code>data</code></dd>

</dl>

add_dis_age: Easily add the disease-related age variable to a data frame

Description

Usage

Value

Arguments

See Also