plotSEMM_setup {plotSEMM} | R Documentation |
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
.
plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22)
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. |
All the parameter estimates required by the arguments are generated from software with the capability of estimating SEMMs.
None.
Bethany E. Kok, Jolynn Pek, Sonya Sterba and Dan Bauer
http://www.bethanykok.com/plotSEMM.html
plotSEMM_contour
,plotSEMM_probability
## 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)