evaluateTree

A helper function to evaluate the negative two log-likelihood (-2LL) of leaf (terminal) nodes for a
dataset. When given a <code>semtree</code> and a unique dataset, the model
estimates -2LL for the tree parameters and data subsets that fit the tree
branching criteria.

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 

evaluateTree function

<dl><dt>tree</dt>
<dd>A fitted <code>semtree</code> object</dd>
<dt>test_set</dt>
<dd>Dataset to fit to a fitted <code>semtree</code> object</dd>
<dt>data_type</dt>
<dd>type of data ("raw", "cov", "cor")</dd>
<dt>leaf_ids</dt>
<dd>Identifies which nodes are leaf nodes. Default is NULL,
which checks model for leaf nodes and fills this information in
automatically.</dd>
<dt>loglik</dt>
<dd>Algorithm to compute log likelihood. The default is 'model' and
refers to a model-based computation. This is preferable because it is more
general. As an alternative, 'mvn' computes the log likelihood based on the
multivariate normal density and the model-implied mean and covariance matrix.</dd></dl>

Arguments

Andreas M. Brandmaier, John J. Prindle

Author

Evaluate Tree -2LL — evaluateTree

<dl>

<dt>tree</dt>
<dd>A fitted <code>semtree</code> object</dd>


<dt>test_set</dt>
<dd>Dataset to fit to a fitted <code>semtree</code> object</dd>


<dt>data_type</dt>
<dd>type of data ("raw", "cov", "cor")</dd>


<dt>leaf_ids</dt>
<dd>Identifies which nodes are leaf nodes. Default is NULL,
which checks model for leaf nodes and fills this information in
automatically.</dd>


<dt>loglik</dt>
<dd>Algorithm to compute log likelihood. The default is 'model' and
refers to a model-based computation. This is preferable because it is more
general. As an alternative, 'mvn' computes the log likelihood based on the
multivariate normal density and the model-implied mean and covariance matrix.</dd>

</dl>

evaluateTree: Evaluate Tree -2LL

Description

Usage

Value

Arguments

Author

References

See Also