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 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 

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>

Format

Simulated Linear Latent Growth Curve Data — lgcm

Simulated Linear Latent Growth Curve Data

lgcm: Simulated Linear Latent Growth Curve Data

Description

Arguments

Format