from_dist_to_loss

Function to transform a distritbution layer output into a loss function

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

from_dist_to_loss function

<dl><dt>family</dt>
<dd>see <code>?deepregression</code></dd>
<dt>ind_fun</dt>
<dd>function applied to the model output before calculating the
log-likelihood. Per default independence is assumed by applying <code>tfd_independent</code>.</dd>
<dt>weights</dt>
<dd>sample weights</dd></dl>

Arguments

Function to transform a distritbution layer output into a loss function — from_dist_to_loss

<dl>

<dt>family</dt>
<dd>see <code>?deepregression</code></dd>


<dt>ind_fun</dt>
<dd>function applied to the model output before calculating the
log-likelihood. Per default independence is assumed by applying <code>tfd_independent</code>.</dd>


<dt>weights</dt>
<dd>sample weights</dd>

</dl>

from_dist_to_loss: Function to transform a distritbution layer output into a loss function

Description

Usage

Value

Arguments