ridge_obj

This function generates an objective function for model-based optimization
based on the cross-validated log-likelihood of a <code>tramnet</code> model with
a ridge penalty only. It is not intended to be called by the user directly,
instead it will be given as an argument to <code>mbo_tramnet</code>.

Partially penalized versions of specific transformation models
implemented in package 'mlt'. Available models include a fully parametric version
of the Cox model, other parametric survival models (Weibull, etc.), models for
binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
linear models, and continuous outcome logistic regression. Hyperparameter tuning
is facilitated through model-based optimization functionalities from package 'mlrMBO'.
The accompanying vignette describes the methodology used in 'tramnet' in detail.
Transformation models and model-based optimization are described in
Hothorn et al. (2019) <doi:10.1111/sjos.12291> and
Bischl et al. (2016) <arxiv:1703.03373>, respectively.

Lucas Kook

tramnet

Penalized Transformation Models

Balint Tamasi

Sandra Siegfried

Samuel Pawel

Torsten Hothorn

ridge_obj function

<dl><dt>object</dt>
<dd>object of class <code>tramnet</code></dd>
<dt>minlambda</dt>
<dd>minimum value for lambda (default: <code>0</code>)</dd>
<dt>maxlambda</dt>
<dd>maximum value for lambda (default: <code>16</code>)</dd>
<dt>folds</dt>
<dd>self specified folds for cross validation (mainly for reproducibility
and comparability purposes)</dd>
<dt>noisy</dt>
<dd>indicates whether folds for k-fold cross-validation should
be random for each iteration, leading to a noisy objective function
(default: <code>FALSE</code>)</dd>
<dt>fold</dt>
<dd>fold for cross validation</dd></dl>

Arguments

Ridge objective function for model based optimization — ridge_obj

<dl>

<dt>object</dt>
<dd>object of class <code>tramnet</code></dd>


<dt>minlambda</dt>
<dd>minimum value for lambda (default: <code>0</code>)</dd>


<dt>maxlambda</dt>
<dd>maximum value for lambda (default: <code>16</code>)</dd>


<dt>folds</dt>
<dd>self specified folds for cross validation (mainly for reproducibility
and comparability purposes)</dd>


<dt>noisy</dt>
<dd>indicates whether folds for k-fold cross-validation should
be random for each iteration, leading to a noisy objective function
(default: <code>FALSE</code>)</dd>


<dt>fold</dt>
<dd>fold for cross validation</dd>

</dl>

ridge_obj: Ridge objective function for model based optimization

Description

Usage

Value

Arguments