evaluate

evaluate.semforest

Evaluates the average deviance (-2LL) of a dataset given a forest.

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 

evaluate function

<dl><dt>x</dt>
<dd>A fitted <code>semforest</code> object</dd>
<dt>data</dt>
<dd>A data.frame</dd>
<dt>...</dt>
<dd>No extra parameters yet.</dd></dl>

Arguments

Author

Average Deviance of a Dataset given a Forest — evaluate

<dl>

<dt>x</dt>
<dd>A fitted <code>semforest</code> object</dd>


<dt>data</dt>
<dd>A data.frame</dd>


<dt>...</dt>
<dd>No extra parameters yet.</dd>

</dl>

evaluate: Average Deviance of a Dataset given a Forest

Description

Usage

Value

Arguments

Author

References

See Also