diversityMatrix

Computes a diversity matrix using a distance function between trees

SEM Trees and SEM Forests -- an extension of model-based decision
trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
split empirical data into homogeneous groups each sharing similar data patterns
with respect to a SEM by recursively selecting optimal predictors of these
differences. SEM forests are an extension of SEM trees. They are ensembles of
SEM trees each built on a random sample of the original data. By aggregating
over a forest, we obtain measures of variable importance that are more robust
than measures from single trees. A description of the method was published by
Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001>
and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.

Andreas M. Brandmaier andy@brandmaier.de

semtree

Recursive Partitioning for Structural Equation Models

Andreas M. Brandmaier 

John J. Prindle 

Manuel Arnold 

Caspar J. Van Lissa 

diversityMatrix function

<dl><dt>forest</dt>
<dd>A SEM forest</dd>
<dt>divergence</dt>
<dd>A divergence function such as hellinger or klsym</dd>
<dt>showProgressBar</dt>
<dd>Boolean. Show a progress bar.</dd></dl>

Arguments

Diversity Matrix — diversityMatrix

<dl>

<dt>forest</dt>
<dd>A SEM forest</dd>


<dt>divergence</dt>
<dd>A divergence function such as hellinger or klsym</dd>


<dt>showProgressBar</dt>
<dd>Boolean. Show a progress bar.</dd>

</dl>

diversityMatrix: Diversity Matrix

Description

Usage

Arguments