precalc_gam

Pre-calculate all gam parts from the list of formulas

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

precalc_gam function

<dl><dt>lof</dt>
<dd>list of formulas</dd>
<dt>data</dt>
<dd>the data list</dd>
<dt>controls</dt>
<dd>controls from deepregression</dd></dl>

Arguments

Pre-calculate all gam parts from the list of formulas — precalc_gam

<dl>

<dt>lof</dt>
<dd>list of formulas</dd>


<dt>data</dt>
<dd>the data list</dd>


<dt>controls</dt>
<dd>controls from deepregression</dd>

</dl>

precalc_gam: Pre-calculate all gam parts from the list of formulas

Description

Usage

Value

Arguments