add_factor_crossing

Add a crossing of two factors 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_crossing function

<dl><dt>data</dt>
<dd>a data frame</dd>
<dt>fac1</dt>
<dd>name of first factor, must be found in <code>df</code></dd>
<dt>fac2</dt>
<dd>name of second factor, must be found in <code>df</code></dd>
<dt>new_name</dt>
<dd>name of the new factor</dd></dl>

Arguments

Add a crossing of two factors to a data frame — add_factor_crossing

<dl>

<dt>data</dt>
<dd>a data frame</dd>


<dt>fac1</dt>
<dd>name of first factor, must be found in <code>df</code></dd>


<dt>fac2</dt>
<dd>name of second factor, must be found in <code>df</code></dd>


<dt>new_name</dt>
<dd>name of the new factor</dd>

</dl>

add_factor_crossing: Add a crossing of two factors to a data frame

Description

Usage

Value

Arguments

See Also