OpenMx 2.0: Extended Structural Equation and Statistical Modeling
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The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.
Keywordsstructural equation modeling path analysis item factor analysis state space modeling mixture distribution latent class analysis optimization big data time series behavior genetics substance use data analysis full information maximum likelihood ordinal data
The authors gratefully acknowledge funding from the National Institutes of Health, specifically Grants R01-DA022989 (PI Boker), R37-DA018673 and R25-DA026119 (PI Neale). Thanks are also due to a large group of beta-testers, including but not limited to: Mike W.-L. Cheung (2014), Charles Driver, Dorothy Bishop, Greg Carey, Pascal Deboeck, Emilio Ferrer, Christopher Hertzog, Kevin Grimm, Ken Kelley, Matthew Keller, Jean-Philippe Laurenceau, Gitta Lubke, John J. McArdle, Sam McQuillin, Sarah Medland, William Revelle, Michael Scharkow, James Steiger, Melissa Sturge-Apple, and Theodore Walls.
- Arminger, G. (1986). Linear stochastic differential equation models for panel data with unobserved variables. Sociological Methodology, 16, 187–212. Retrieved November 26, 2014, from http://www.jstor.org/stable/270923.
- Bates, T. C. (2013). umx: A help package for structural equation modeling in openmx [Computer software manual], Edinburgh, UK. Retrieved November 26, 2014, from http://github.com/tbates/umx/ (version 0.6).
- Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., ... Fox, J. (2009). OpenMx: Multipurpose software for statistical modeling, University of Virginia, Department of Psychology, Charlottesville, VA. Retrieved November 26, 2014, from http://openmx.psyc.virginia.edu.
- Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., ... Fox, J. (2012). OpenMx: Multipurpose software for statistical modeling, version 1.2, University of Virginia, Department of Psychology, Charlottesville, VA. Retrieved November 26, 2014, from http://openmx.psyc.virginia.edu.
- Browne, M., & Zhang, G. (2010). DyFA 3.00 user guide. Retrieved December 2, 2014, from http://faculty.psy.ohio-state.edu/browne/software.php.
- Cheung, M. W.-L. (2014). metaSEM: Meta-analysis using structural equation modeling [Computer software manual]. Retrieved November 26, 2014, from OpenMx 2.0 28 http://courses.nus.edu.sg/course/psycwlm/Internet/metaSEM/ (R package version 0.9-0).
- Dolan, C. V. (2005). MKFM6: Multi-group, multi-subject stationary time series modeling based on the Kalman filter. Retrieved November 27, 2014, from http://tinyurl.com/MKFM6Dolan.
- Ghalanos, A., & Theussl, S. (2012). RSOLNP: General non-linear optimization using augmented lagrange multiplier method [Computer software manual]. (R package version 1.14.).Google Scholar
- Gill, P. E., Murray, W., Saunders, M. A., & Wright, M. H. (1986). User’s guide for NPSOL (version 4.0): A FORTRAN package for nonlinear programming (Technical Report), Department of Operations Research, Stanford University.Google Scholar
- Hamagami, F., & McArdle, J. J. (2007). Dynamic extensions of latent difference score models. In S. M. Boker & M. J. Wenger (Eds.), Data analytic techniques for dynamical systems. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
- Hunter, M. D. (2012, July 9–12). The addition of LISREL specification to OpenMx. Presented at the 2012 Annual International Meeting of the Psychometric Society, Lincoln, NE.Google Scholar
- Hunter, M. D. (2014, May 22–25). Extended structural equations and state space models OpenMx 2.0.29 when data are missing at random. Presented at the 2014 Annual Meeting of the Association for Psychological Science, San Francisco, CA.Google Scholar
- Ihaka, R., & Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.Google Scholar
- Johnson, S. G. (2010). The NLopt nonlinear-optimization package. R package. Retrieved November 26, 2014, from http://ab-initio.mit.edu/nlopt.
- Jöreskog, K. G., & Sörbom, D. (1999). Lisrel 8: User’s reference guide. Lincolnwood, IL: Scientific Software International.Google Scholar
- Jöreskog, K. G., & Van Thillo, M. (1972). LISREL: A general computer program for estimating a linear structural equation system involving multiple indicators of unmeasured variables. ETS Research Bulletin Series. doi: 10.1002/j.2333-8504.1972.tb00827.x.
- MATLAB. (2014). Version 8.3 (R2014a). Natick, MA: The MathWorks Inc.Google Scholar
- McArdle, J. J., & Boker, S. (1990). Rampath. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
- McArdle, J. J., & Hamagami, F. (2001). Linear dynamic analyses of incomplete longitudinal data. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 137–176). Washington, DC: American Psychological Association.Google Scholar
- McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37, 234–251.Google Scholar
- Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical modeling (6th ed.). Richmond, VA: Department of Psychiatry, VCU.Google Scholar
- Pritikin, J. N., Hunter, M. D., & Boker, S. (in press). Modular open-source software for Item Factor Analysis. Educational and Psychological Measurement.Google Scholar
- R Core Team. (2014). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved November 26, 2014, from http://www.R-project.org/.
- Schmidberger, M., Morgan, M., Eddelbuettel, D., Yu, H., Tierney, L., & Mansmann, U. (2009). State-of-the-art in parallel computing with R. Journal of Statistical Software, 47(1).Google Scholar
- The Numerical Algorithms Group (NAG). (n.d.). The NAG Library. Retrieved November 26, 2014, from http://www.nag.com. Oxford, UK.
- von Oertzen, T., Brandmaier, A., & Tsang, S. (in press). Structural equation modeling with \(\omega \)nyx. Structural Equation Modeling: A Multidisciplinary Journal.Google Scholar
- Whaley, R. C., & Dongarra, J. J. (1998). Automatically tuned linear algebra software. In: Proceedings of the 1998 ACM/IEEE conference on supercomputing (pp. 1–27).Google Scholar
- Ye, Y. (1987). Interior algorithms for linear, quadratic, and linearly constrained non-linear programming (Unpublished doctoral dissertation). Department of ESS, Stanford University.Google Scholar