adjusted_c_hat

Creates the <code>c_hat</code> input for <code>lgp</code>,
so that it accounts for normalization between data points in the
<code>"poisson"</code> or <code>"nb"</code> observation model

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

adjusted_c_hat function

<dl><dt>y</dt>
<dd>response variable, vector of length <code>n</code></dd>
<dt>norm_factors</dt>
<dd>normalization factors, vector of length <code>n</code></dd></dl>

Arguments

Set the GP mean vector, taking TMM or other normalization
into account — adjusted_c_hat

<dl>

<dt>y</dt>
<dd>response variable, vector of length <code>n</code></dd>


<dt>norm_factors</dt>
<dd>normalization factors, vector of length <code>n</code></dd>

</dl>

adjusted_c_hat: Set the GP mean vector, taking TMM or other normalization into account

Description

Usage

Value

Arguments

See Also