Nonlinear Relations among Latent Variables

Jolynn Pek, Sonya K. Sterba, Bethany E. Kok and Daniel J. Bauer
University of North Carolina at Chapel Hill


This web page generates semiparametric estimates of the regression function for two latent variables. A mixture of linear structural equation models must first be fit to the data. The mixing probabilities (π) and latent variable model parameters (labeled in the diagram below) are then input into the boxes provided.

Empirical example on positive emotions and heuristic processing Mplus output Mx output

Empirical example on negative emotions and heuristic processing Mplus output Mx output

                          

 Label for η1:     Label for η2:
 Number of classes:
 Class 1: π = α1 = α2 = β21 = ψ11 = ψ22 =
Show class information


plotSEMM

Users who wish to generate these plots within the R environment can do so by directly downloading the R-package, plotSEMM.
plotSEMM generates the same plots with higher graphical quality in addition to customizable options.
plotSEMM is written by Bethany E. Kok, Jolynn Pek, Sonya K. Sterba and Daniel J. Bauer and maintained by Bethany E. Kok.

R is a free software environment for statistical computing and graphics and can be obtained here.
plotSEMM may also be obtained from the Comprehensive R Archive Network .

This work was supported by the National Science Foundation (award SES-0716555 to D. J. Bauer).



References

Pek, J., Sterba, S. K., Kok, B. E., & Bauer, D. J. (in review). Estimating and visualizing nonlinear relations among latent variables: A semiparametric approach.

Bauer, D. J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12, 513-535.

Neale, M. C., Boker, S. M., Xie, G. & Maes, H. H. (2003). Mx: Statistical modeling (6th Ed.). Richmond, VA: Department of Psychiatry.




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Last updated 19 September 2008