testdata_002

Medium-size artificial test data, used mostly for tutorials

datasets

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

testdata_002 function

A data frame with 96 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>individual id, a factor with levels: 01-12</dd> <dt>age</dt>
<dd>age</dd> <dt>diseaseAge</dt>
<dd>disease-related age</dd> <dt>sex</dt>
<dd>a factor with 2 levels: Male, Female</dd> <dt>group</dt>
<dd>a factor with 2 levels: Case, Control</dd> <dt>y</dt>
<dd>a continuous variable</dd>
</dl>

Format

Medium-size artificial test data, used mostly for tutorials — testdata_002

A data frame with 96 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>individual id, a factor with levels: 01-12</dd>

 <dt>age</dt>
<dd>age</dd>

 <dt>diseaseAge</dt>
<dd>disease-related age</dd>

 <dt>sex</dt>
<dd>a factor with 2 levels: Male, Female</dd>

 <dt>group</dt>
<dd>a factor with 2 levels: Case, Control</dd>

 <dt>y</dt>
<dd>a continuous variable</dd>


</dl>

testdata_002: Medium-size artificial test data, used mostly for tutorials

Description

Usage

Arguments

Format

See Also