plotSEMM_setup {plotSEMM}R Documentation

Set up function for plotSEMM

Description

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.

Usage

plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22)

Arguments

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.

Details

All the parameter estimates required by the arguments are generated from software with the capability of estimating SEMMs.

Value

None.

Author(s)

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

References

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

See Also

plotSEMM_contour,plotSEMM_probability

Examples

## 2 class empirical example on positive emotions and heuristic processing in Pek, Sterba, Kok & Bauer (XXXX)

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)

plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22)




[Package plotSEMM version 1.0 Index]