plotSEMM-package {plotSEMM}R Documentation

Graphing nonlinear relations among latent variables from Structural Equation Mixture Models

Description

Contains functions plotSEMM_setup, plotSEMM_contour, plotSEMM_probability and plotSEMM_legend. Creates plots which accompany Bauers (2005) semiparametric method of modeling Structural Equation Mixture Models (SEMMs) by allowing researchers to visualize potential nonlinear relationships between a latent predictor and outcome.

Details

Package: plotSEMM
Type: Package
Version: 1.0
Date: 2008-03-11
License: GPL (>= 2)

Contains four functions:
plotSEMM_setup takes and organizes user input generated from SEMM software such as Mplus (Muthen & Muthen, 2007), Mx (Neale, Boker, Xie & Maes, 2004) or MECOSA (Arminger, Wittenberg, & Schepers, 1996) in Gauss. The user needs to specify 6 vectors of model estimates, each containing K elements where K is the number of classes estimated. These vectors are specified to include (1) the marginal class probabilities, pi (2) the class means for the latent predictor, alpha1; (3) the intercepts and (4) slopes from the within-class regression of the latent outcome on the latent predictor, alpha2 and beta21; (5) the within class variances of the latent predictor, psi11; and (6) the within-class residual variances of the latent outcome, psi22.
plotSEMM_contour generates (a) the potential nonlinear regression function; (b) bivariate distribution of the latent variables; (c) marginal distributions of the latent variables; (d) within class linear regression functions; and (e) within class marginal distributions for the latent variables.
plotSEMM_probability generates a plot which expresses the mixing probabilities for each latent class conditioned on the exogenous latent variable, working from the data structure provided by plotSEMM_setup.
plotSEMM_legend generates an optional legend to accompany the above plots.

Author(s)

Bethany E. Kok, Jolynn Pek, Sonya Sterba and Dan Bauer

Maintainer: Bethany E. Kok bethanyk@unc.edu

References

Bauer, D.J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 12(4), 513-535.

Pek, J., Sterba, S., Kok, B.E. & Bauer, D.J. (in prep). Visualizing nonlinear relations among latent variables: An online plotting utility and R package.

http://www.bethanykok.com/plotSEMM.html


[Package plotSEMM version 1.0 Index]