semtree

What is this?

An R package for estimating Structural Equation Model (SEM) Trees and Forests. They are a fusion of SEM and decision trees, or SEM and random forests respectively. While SEM is a confirmatory modeling technique, SEM trees and forests allow to explore whether there are predictors that provide further information about an initial, theory-based model. Potential use cases are the search for potential predictors that explain individual differences, finding omitted variables in a model, or exploring measurement invariance over a large set of predictors. A recent overview is in our latest book chapter in the SEM handbook (Brandmaier & Jacobucci, 2023).

Install

Install the latest stable version from CRAN:

install.packages("semtree")

To install the latest semtree package directly from GitHub, copy the following line into R:

library(devtools)
devtools::install_github("brandmaier/semtree")

# even better: install with package vignette (extra documentation)
devtools::install_github("brandmaier/semtree",force=TRUE, build_opts = c())

Usage

Package documentation and use-cases with runnable R code can be found on our github pages: https://brandmaier.github.io/semtree/.

Package vignettes (shipped with the package) contain documentation on how to use the package. Simply type this in R once you have loaded the package:

browseVignettes("semtree")

References

Theory and method:

  • Brandmaier, A. M., & Jacobucci, R. C. (2023). Machine-learning approaches to structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd rev. ed., pp. 722–739). Guilford Press.

  • Arnold, M., Voelkle, M.C., and Brandmaier, A.M. (2021). Score-guided structural equation model trees. Frontiers in psychology, 11,

  • Brandmaier, A. M., Driver, C., & Voelkle, M. C. (2019). 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.

Applied examples (there are many more):

Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (2017). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates. Developmental Psychology.