nlsem-package: Fitting structural equation mixture models
In nlsem: Fitting Structural Equation Mixture Models
nlsem-package R Documentation
Fitting structural equation mixture models
Description
Estimation of structural equation models with nonlinear effects and underlying
nonnormal distributions.
Details
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.
Future Features
NSEMM, LMS and QML for more than one latent endogenous variable.
Parameter standardization.
References
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
nlsem documentation built on Aug. 31, 2023, 5:14 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| nlsem-package | R Documentation |
Fitting structural equation mixture models
Description
Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.
Details
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.
Future Features
NSEMM, LMS and QML for more than one latent endogenous variable.
Parameter standardization.
References
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.