gam_plot_data

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

gam_plot_data function

<dl><dt>pp</dt>
<dd>processed term</dd>
<dt>weights</dt>
<dd>layer weights</dd>
<dt>grid_length</dt>
<dd>length for grid for evaluating basis</dd>
<dt>pe_fun</dt>
<dd>function used to generate partial effects</dd></dl>

Arguments

used by gam_processor — gam_plot_data

<dl>

<dt>pp</dt>
<dd>processed term</dd>


<dt>weights</dt>
<dd>layer weights</dd>


<dt>grid_length</dt>
<dd>length for grid for evaluating basis</dd>


<dt>pe_fun</dt>
<dd>function used to generate partial effects</dd>

</dl>

used by gam_processor

gam_plot_data: used by gam_processor

Description

Usage

Arguments