testdata_001

A very small artificial test data, used mostly for unit tests

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_001 function

A data frame with 24 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>individual id, a factor with levels: 1, 2, 3, 4</dd> <dt>age</dt>
<dd>age</dd> <dt>dis_age</dt>
<dd>disease-related age</dd> <dt>blood</dt>
<dd>a continuous variable</dd> <dt>sex</dt>
<dd>a factor with 2 levels: Male, Female</dd> <dt>y</dt>
<dd>a continuous variable</dd>
</dl>

Format

A very small artificial test data, used mostly for unit tests — testdata_001

A data frame with 24 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>individual id, a factor with levels: 1, 2, 3, 4</dd>

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

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

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

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

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


</dl>

Last chance! 50% off unlimited learning

testdata_001: A very small artificial test data, used mostly for unit tests

Description

Usage

Arguments

Format

See Also