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.
An R package for estimating Structural Equation Model Trees and Forests.
To install the latest semtree package directly from GitHub, copy the following line into R:
Please see official semtree website: http://brandmaier.de/semtree
Theory and method:
Brandmaier, A. M., Driver, C., & Voelkle, M. C. (in press). Recursive partitioning in continuous time analysis. In K. van Montfort, J. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences. New York: Springer.
Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21, 566-582.
Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2014). Exploratory data mining with structural equation model trees. In J. J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 96-127). New York: Routledge.
Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71-86.
Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (in press). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates. Developmental Psychology.