re_layer

pen_layer

random effect layer
trainable penalty layer

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

re_layer function

<dl><dt>units</dt>
<dd>integer; number of units</dd>
<dt>...</dt>
<dd>arguments passed to TensorFlow layer</dd>
<dt>P</dt>
<dd>penalty matrix</dd></dl>

Arguments

random effect layer — re_layer

<dl>

<dt>units</dt>
<dd>integer; number of units</dd>


<dt>...</dt>
<dd>arguments passed to TensorFlow layer</dd>


<dt>P</dt>
<dd>penalty matrix</dd>

</dl>

re_layer: random effect layer

Description

Usage

Value

Arguments