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Fitting Nonlinear Structural Equation Models in R with Package nlsem

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  • Umbach, Nora
  • Naumann, Katharina
  • Brandt, Holger
  • Kelava, Augustin

Abstract

Structural equation mixture modeling (SEMM) has become a standard procedure in latent variable modeling over the last two decades (Jedidi, Jagpal, and DeSarbo'97b; Muthén and Shedden'99; Muthén 2001, 2004; Muthén and Asparouhov 2009). SEMM was proposed as a technique for the approximation of nonlinear latent variable relationships by finite mixtures of linear relationships (Bauer 2005, 2007; Bauer, Baldasaro, and Gottfredson 2012). In addition to this semiparametric approach to nonlinear latent variable modeling, there are numerous parametric nonlinear approaches for normally distributed variables (e.g., LMS in Mplus; Klein and Moosbrugger 2000). Recently, an additional semiparametric nonlinear structural equation mixture modeling (NSEMM) approach was proposed by Kelava, Nagengast, and Brandt (2014) that is capable of dealing with nonnormal predictors. In the nlsem package presented here, the SEMM, two distribution analytic (QML and LMS) and NSEMM approaches can be specified and estimated. We provide examples of how to use the package in the context of nonlinear latent variable modeling.

Suggested Citation

  • Umbach, Nora & Naumann, Katharina & Brandt, Holger & Kelava, Augustin, 2017. "Fitting Nonlinear Structural Equation Models in R with Package nlsem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i07).
    • Handle: RePEc:jss:jstsof:v:077:i07
    DOI: http://hdl.handle.net/10.18637/jss.v077.i07
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    References listed on IDEAS

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    1. Conor Dolan & Han Maas, 1998. "Fitting multivariage normal finite mixtures subject to structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 227-253, September.
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    3. Kim, Gwangsu & Choi, Taeryon, 2019. "Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 68-82.

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