predict.semforest

Predict method for semtree and semforest

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 

predict.semforest function

<dl><dt>object</dt>
<dd>Object of class <code>semtree</code> or `semforest`.</dd>
<dt>data</dt>
<dd>New test data of class <code>data.frame</code>. If no data is provided,
attempts to extract the data from the object.</dd>
<dt>type</dt>
<dd>Type of prediction. One of `c('node_id')`. See Details.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Caspar J. van Lissa, Andreas Brandmaier

Author

Predict method for semtree and semforest — predict.semforest

<dl>

<dt>object</dt>
<dd>Object of class <code>semtree</code> or `semforest`.</dd>


<dt>data</dt>
<dd>New test data of class <code>data.frame</code>. If no data is provided,
attempts to extract the data from the object.</dd>


<dt>type</dt>
<dd>Type of prediction. One of `c('node_id')`. See Details.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

predict.semforest: Predict method for semtree and semforest

Description

Usage

Value

Arguments

Author