Description Details Future Features References

Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.

This is a package for estimating nonlinear structural equation mixture models using an expectation-maximization (EM) algorithm. Four different approaches are implemented. Firstly, the Latent Moderated Structural Equations (LMS) approach (Klein & Moosbrugger, 2000) and the Quasi-Maximum Likelihood (QML) approach (Klein & Muthen, 2007), which allow for two-way interaction and quadratic terms in the structural model. Due to the nonlinearity, the latent criterion variables cannot be assumed to be normally distributed. Therefore, the latent criterins's distribution is approximated with a mixture of normal distributions in LMS. Secondly, the Structural Equation finite Mixture Model (STEMM or SEMM) approach (Jedidi, Jagpal & DeSarbo, 1997), which uses mixtures to model latent classes. In this way it can deal with heterogeneity in the sample or nonlinearity and nonnormality of the latent variables and their indicators. And thirdly, a combination of these two approaches, the Nonlinear Structural Equation Mixture Model (NSEMM) approach (Kelava, Nagengast & Brandt, 2014). Here, interaction and quadratic terms as well as latent classes can be modeled.

The models can be specified with `specify_sem`

. Depending
on the specification of `interaction`

and the number of latent classes
(`num.classes`

) the returned object will be of class
`singleClass`

, `semm`

, or `nsemm`

. Each of these can be
estimated using `em`

and models of type `singleClass`

can additionally be fitted with the function `qml`

.

NSEMM, LMS and QML for more than one latent endogenous variable.

Parameter standardization.

Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). STEMM: A General
Finite Mixture Structural Equation Model, *Journal of
Classification, 14*, 23–50. doi:http://dx.doi.org/10.1007/s003579900002

Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural
equation mixture modeling approach for non-normally distributed latent
predictor variables. *Structural Equation Modeling, 21*, 468-481.
doi:http://dx.doi.org/10.1080/10705511.2014.915379

Klein, A. &, Moosbrugger, H. (2000). Maximum likelihood estimation of
latent interaction effects with the LMS method. *Psychometrika, 65*,
457–474. doi:http://dx.doi.org/10.1007/bf02296338

Klein, A. &, Muthen, B. O. (2007). Quasi-Maximum Likelihood Estimation of
Structural Equation Models With Multiple Interaction and Quadratic
Effects. *Multivariate Behavior Research, 42*, 647–673.
doi:http://dx.doi.org/10.1080/00273170701710205

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