predict.4pl

Use new input data and a fitted four parameter logistic
model to predict corresponding outcomes.

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

predict.4pl function

<dl><dt>object</dt>
<dd>Fitted <code>4pl</code> model</dd>
<dt>Z</dt>
<dd>Numeric vector of new input samples</dd>
<dt>...</dt>
<dd>Ignored additional parameters</dd></dl>

Arguments

Prediction for 4pL models — predict.4pl

<dl>

<dt>object</dt>
<dd>Fitted <code>4pl</code> model</dd>


<dt>Z</dt>
<dd>Numeric vector of new input samples</dd>


<dt>...</dt>
<dd>Ignored additional parameters</dd>

</dl>

predict.4pl: Prediction for 4pL models

Description

Usage

Value

Arguments