GMM with classes in addition to LGM PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Raheem Paxton posted on Saturday, April 15, 2006 - 1:12 am
Not sure what to do if I wanted to create my classes, a LGM and examine the relationship between classes and the intercept and slope. Can you tell me what I am doing wrong. Thanks

usev are a-g;
!where a-d are outcome variables and e-g !are variables for the classes

classes = c (2);
Analysis: type=mixture;
starts = 50 2;
model: %overall%

i s| a@0 b@1 c@2 d@3;
c on i s;
series = e(1) f(2) g(3);
 Bengt O. Muthen posted on Saturday, April 15, 2006 - 6:12 am
Typically, c influences i and s, not the other way around. The c influence on i and s is such that i and s have different means across the classes. You will have this model as the default if you say only

i s | etc

in the overall part of the model.

If in contrast you really intend to have c influence i and s, you should have observed indicators of c. And you should also specify the model so that the means of i and s do not vary across the classes - if not, you have a reciprocal interaction loop problem where c influences i and s and i and s influence c.
 Diane Chen posted on Monday, November 22, 2010 - 7:57 am
I am interested in looking at how class membership for one GGMM model (2-class, treated as observed/hard binned) predicts intercept/slope in LGM for another variable.
Here is my input:
vi vs vq| Var14t@-4 Var15t@-3 Var16t@-2 Var17t@-1 Var18t@0 Var19t@1 Var20t@2
Var21t@3 Var22t@4 Var23t@5 Var24t@6 Var25t@7;

vi vs vq ON RPIclass;

Results indicate that vi ON RPIclass is significant Est/SE = -2.376, p=.018. I'm not sure how exactly to interpret this finding. Any help would be appreciated. Thanks!
 Bengt O. Muthen posted on Monday, November 22, 2010 - 5:31 pm
If say RPIclass=1 means high trajectory vs 0 being low trajectory, this says that a high trajectory membership leads to a lower intercept growth factor than the low trajectory membership.
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