semforest

print.semforest

plot.semforest

A template SEM. Same as in <code>semtree</code>.

model

A dataframe to create a forest from. Same as in <code>semtree</code>.

data

A semforest control object to set forest parameters.

control

An optional list of covariates. See semtree code example.

predictors

constraints

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Grows a SEM Forest from a template model and a dataset. This may take some
time.

multivariate

models

tree

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>).

semforest: Create a SEM Forest

Description

Usage

Arguments

Value

References

See Also