extract_pure_gam_part

Extract the smooth term from a deepregression term specification

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

extract_pure_gam_part function

<dl><dt>term</dt>
<dd>term specified in a formula</dd>
<dt>remove_other_options</dt>
<dd>logical; whether to remove other options
withing the smooth term</dd></dl>

Arguments

Extract the smooth term from a deepregression term specification — extract_pure_gam_part

<dl>

<dt>term</dt>
<dd>term specified in a formula</dd>


<dt>remove_other_options</dt>
<dd>logical; whether to remove other options
withing the smooth term</dd>

</dl>

extract_pure_gam_part: Extract the smooth term from a deepregression term specification

Description

Usage

Value

Arguments