logicDT.bagging

logicDT.bagging.default

logicDT.bagging.formula

Function for fitting bagged logicDT models.

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

logicDT.bagging function

<dl><dt>X</dt>
<dd>Matrix or data frame of binary predictors coded as 0 or 1.</dd>
<dt>y</dt>
<dd>Response vector. 0-1 coding for binary responses.
Otherwise, a regression task is assumed.</dd>
<dt>Z</dt>
<dd>Optional matrix or data frame of quantitative/continuous
covariables. Multiple covariables allowed for splitting the trees.
If leaf regression models (such as four parameter logistic models) shall
be fitted, only the first given covariable is used.</dd>
<dt>bagging.iter</dt>
<dd>Number of bagging iterations</dd>
<dt>...</dt>
<dd>Arguments passed to <code>logicDT</code></dd>
<dt>formula</dt>
<dd>An object of type <code>formula</code> describing the
model to be fitted.</dd>
<dt>data</dt>
<dd>A data frame containing the data for the corresponding
<code>formula</code> object. Must also contain quantitative covariables
if they should be included as well.</dd></dl>

Arguments

Fitting bagged logicDT models — logicDT.bagging

<dl>

<dt>X</dt>
<dd>Matrix or data frame of binary predictors coded as 0 or 1.</dd>


<dt>y</dt>
<dd>Response vector. 0-1 coding for binary responses.
Otherwise, a regression task is assumed.</dd>


<dt>Z</dt>
<dd>Optional matrix or data frame of quantitative/continuous
covariables. Multiple covariables allowed for splitting the trees.
If leaf regression models (such as four parameter logistic models) shall
be fitted, only the first given covariable is used.</dd>


<dt>bagging.iter</dt>
<dd>Number of bagging iterations</dd>


<dt>...</dt>
<dd>Arguments passed to <code>logicDT</code></dd>


<dt>formula</dt>
<dd>An object of type <code>formula</code> describing the
model to be fitted.</dd>


<dt>data</dt>
<dd>A data frame containing the data for the corresponding
<code>formula</code> object. Must also contain quantitative covariables
if they should be included as well.</dd>

</dl>

logicDT.bagging: Fitting bagged logicDT models

Description

Usage

Value

Arguments

Details