lgcm

<p>This data set provides simple data to fit with a LGCM.</p>

datasets

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 

lgcm function

<p><code>lgcm</code> is a matrix containing 400 rows and 8 columns of simulated data. Longitudinal observations are o1-o5. Covariates are agegroup, training, and noise.</p>

lgcm: Simulated Linear Latent Growth Curve Data

Description

Usage

Arguments

Format