Hi, I am new to MPlus and I am a bit lost in all the information available. I am interested in identifying different trajectories of a continuous indicator (3 time-points), as well as a set of cavariates that predicts class inclusion. Finally, I want to see if the trajectories have different consequences (distal outcomes at T3 - continuous). Below is my syntax:
int slp ON sex Excl1 Vict1 PrSoc1 F_Inv1 FQ1 M_SSup1 M_NgI1 M_Pow1;
C#1-C#3 ON sex Excl1 Vict1 PrSoc1 F_Inv1 FQ1 M_SSup1 M_NgI1 M_Pow1;
%c#1% int; slp;
%c#2% int; slp;
%c#3% int; slp;
%c#4% int; slp;
My questions are: 1. Is it necessary to include the regression command of the intercept and slope in all classes (or just in the Overall model)? 2. How to compare the trajectories regarding the distal outcomes?
1. The standard and more parsimonious approach is to not let the regression vary across classes, so you specify it only in Overall.
2.The distal outcome means will vary across classes as the default. If you want to test mean differences you label these means and then compute their difference in Model Constraint. if you don't want to include the distal in the model, you can study these means using the Auxiliary option DCON.
Note that as a first step you may want to use the default of class-invariant variances and therefore not mention int, slp in each class.
I'm trying to perform the 3 step method, but I can't seem to find the "Logits for the Classification Probabilities the Most Likely Latent Class Membership (Row) by Latent Class (Column)" table. I can only find the "Average Latent Class Probabilities". Can you tell me why this happens?
Also, if my original model has an entropy of .889 (sample size =260), do you think there will be severe bias if I choose the AUXILIARY=(E) option?
I don't think we added these logits until Version 7.11.
Anne Arnett posted on Tuesday, August 12, 2014 - 10:27 am
I am fitting a growth mixture model with a continuous outcome (a3-a15, 5 time points measured at different ages for each participant). Latent classes are predicted by a continuous variable (x). I also have a distal binary outcome. My data includes siblings, so the model TYPE= MIXTURE RANDOM COMPLEX. The model is:
MODEL: %OVERALL% i s | a3-a15 AT cage3-cage15; i s on x; C on x;
My question is, is it necessary to include the statement 'i s on x'? When I do this with more than 2 classes estimated, I get a non-positive PSI, and when I remedy this by fixing s@0, it makes it very difficult to replicate the -LL. What would it mean to have x only predicting C, rather than all of the latent variables?