Description Usage Arguments Details Author(s) References See Also Examples
View source: R/plotSEMM_setup.R
Takes 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 and generates model predicted data for processing in graphing functions
plotSEMM_contour
and plotSEMM_probability
. Reterns a data.frame
to be passed to other functions in the package.
1 | plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22, points = 50)
|
pi |
Vector: K marginal class probabilities. |
alpha1 |
Vector: K means of the latent predictor. |
alpha2 |
Vector: K inercepts slopes from the within-class regression of the latent outcome on the latent predictor. |
beta21 |
Vector: K slopes from the within-class regression of the latent outcome on the latent predictor. |
psi11 |
Vector: K within-class variances of the latent predictor. |
psi22 |
Vector: K within-class variances of the latent outcome. |
points |
number of points to use. Default is 50 |
All the parameter estimates required by the arguments are generated from software with the capability of estimating SEMMs.
Bethany Kok and Phil Chalmers rphilip.chalmers@gmail.com
Pek, J. & Chalmers, R. P. (2015). Diagnosing Nonlinearity With Confidence Envelopes for a Semiparametric Approach to Modeling Bivariate Nonlinear Relations Among Latent Variables. Structural Equation Modeling, 22, 288-293. doi: 10.1080/10705511.2014.937790
Pek, J., Chalmers, R. P., Kok B. E., & Losardo, D. (2015). Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables. Journal of Educational and Behavioral Statistics, 40, 402-423. doi: 10.3102/1076998615589129
plotSEMM_contour
,plotSEMM_probability
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
# 2 class empirical example on positive emotions and heuristic processing
# in Pek, Sterba, Kok & Bauer (2009)
pi <- c(0.602, 0.398)
alpha1 <- c(3.529, 2.317)
alpha2 <- c(0.02, 0.336)
beta21 <- c(0.152, 0.053)
psi11 <- c(0.265, 0.265)
psi22 <- c(0.023, 0.023)
plotobj <- plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.