loop_through_pfc_and_call_trafo

Function to loop through parsed formulas and apply data trafo

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

loop_through_pfc_and_call_trafo function

<dl><dt>pfc</dt>
<dd>list of processor transformed formulas</dd>
<dt>newdata</dt>
<dd>list in the same format as the original data</dd>
<dt>engine</dt>
<dd>character; the engine which is used to setup the NN (tf or torch)</dd></dl>

Arguments

Function to loop through parsed formulas and apply data trafo — loop_through_pfc_and_call_trafo

<dl>

<dt>pfc</dt>
<dd>list of processor transformed formulas</dd>


<dt>newdata</dt>
<dd>list in the same format as the original data</dd>


<dt>engine</dt>
<dd>character; the engine which is used to setup the NN (tf or torch)</dd>

</dl>

loop_through_pfc_and_call_trafo: Function to loop through parsed formulas and apply data trafo

Description

Usage

Value

Arguments