handle_gam_term

Function to define smoothness and call mgcv's smooth constructor

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

handle_gam_term function

<dl><dt>object</dt>
<dd>character defining the model term</dd>
<dt>data</dt>
<dd>data.frame or list</dd>
<dt>controls</dt>
<dd>controls for penalization</dd></dl>

Arguments

Function to define smoothness and call mgcv's smooth constructor — handle_gam_term

<dl>

<dt>object</dt>
<dd>character defining the model term</dd>


<dt>data</dt>
<dd>data.frame or list</dd>


<dt>controls</dt>
<dd>controls for penalization</dd>

</dl>

handle_gam_term: Function to define smoothness and call mgcv's smooth constructor

Description

Usage

Value

Arguments