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

An object of class "cluster" representing a parallel socket cluster. See package <a rd-options="parallel" href="/link/makeCluster?package=semtree&version=0.9.12&to=parallel" data-mini-rdoc="parallel::makeCluster">makeCluster</a>.

cluster

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

 multivariate

 tree

 models

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 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.

Andreas M. Brandmaier andy@brandmaier.de

semtree

Recursive Partitioning for Structural Equation Models

Andreas M. Brandmaier 

John J. Prindle 

semforest function

An object of class "cluster" representing a parallel socket cluster. See package <a rd-options='parallel' href='makeCluster'>makeCluster</a>.

semforest: Create a SEM Forest

Description

Usage

Arguments

Value

References

See Also