refitTrees

Newly fit the decision trees in the <code>logicDT</code> model using
the supplied tree control parameters.
This is especially useful if, e.g., the model was initially trained
without utilizing a continuous covariable or fitting linear models and
now 4pL model shall be fitted.

A statistical learning method that tries to find the best set
of predictors and interactions between predictors for modeling binary or
quantitative response data in a decision tree. Several search algorithms
and ensembling techniques are implemented allowing for finetuning the
method to the specific problem. Interactions with quantitative
covariables can be properly taken into account by fitting local
regression models. Moreover, a variable importance measure for assessing
marginal and interaction effects is provided. Implements the
procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Michael Lau

logicDT

Identifying Interactions Between Binary Predictors

refitTrees function

<dl><dt>model</dt>
<dd>A fitted <code>logicDT</code> model</dd>
<dt>tree_control</dt>
<dd>Tree control parameters. This object should be
constructed using the function <code>tree.control</code>.
Alternatively, the old <code>tree_control</code> from <code>model</code> can be
modified and specified here.</dd></dl>

Arguments

Refit the logic decision trees — refitTrees

<dl>

<dt>model</dt>
<dd>A fitted <code>logicDT</code> model</dd>


<dt>tree_control</dt>
<dd>Tree control parameters. This object should be
constructed using the function <code>tree.control</code>.
Alternatively, the old <code>tree_control</code> from <code>model</code> can be
modified and specified here.</dd>

</dl>

refitTrees: Refit the logic decision trees

Description

Usage

Value

Arguments