GMM with predictor covariates and dis... PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Miguel Freitas posted on Monday, June 02, 2014 - 9:44 am
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 | sw1@0 sw2@1 sw3@2;
int WITH slp;

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;





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?

Thank you!
 Bengt O. Muthen posted on Tuesday, June 03, 2014 - 5:58 pm
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.
 Miguel Freitas posted on Wednesday, June 04, 2014 - 3:51 am
Hello, Professor,

Thank you for your reply!

I've tried using your suggestion, but I keep getting this error message:

*** ERROR in VARIABLE command
Unrecognized type in the AUXILIARY option: DCON

The syntax is as follows (I've tried different possibilities, though, like writing the DCON option after every variable name, and the result was the same):

AUXILIARY=(DCON) actout3 shy3 lrnprob3 sc_comp3 soc_ac3 behav3;

Also, when I use the E or the DU3STEP options, I don't get any errors.

Thank you!
 Linda K. Muthen posted on Wednesday, June 04, 2014 - 11:32 am
DCON was introduced in Version 7.1. Perhaps you are using an earlier version.
 Miguel Freitas posted on Wednesday, June 04, 2014 - 11:49 am
Yes, in fact, I'm using version 7.

In that case, what is your recomendation? Is the AUXILIARY=(E) option ok (contrary to what I said before, the DU3STEP option doesn't work either)?

 Bengt O. Muthen posted on Wednesday, June 04, 2014 - 6:30 pm
I would recommend getting Version 7.2. E and DU3STEP have been replaced by a better method. See also Web Note 15 on our website for a manual 3-step method.
 Miguel Freitas posted on Thursday, June 05, 2014 - 5:34 am
Thank you, Professor,

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?

Thank you!
 Linda K. Muthen posted on Thursday, June 05, 2014 - 1:34 pm
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:

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?

Thank you.
 Bengt O. Muthen posted on Tuesday, August 12, 2014 - 3:24 pm
i s on x;

says that these growth factors vary as a function of x also within classes. So for instance, a high x value tends to create a high i, a high starting point, within each of the classes.

You can check BIC to see if i s on x; is needed.
 Anna King posted on Tuesday, September 16, 2014 - 1:35 pm
Dear Professor,

I'm trying to use the PC method to analyze a GMM model. I included AUXILIARY into the VARIABLE part. I don't know why it didn't work. It kept telling me that X is an unknown variable. Could you please tell me why? Below is the codes I wrote. Thanks!

Title: PC Method

NAMES = y1-y4 x;
CLASSES = c(2);

FILE IS Math.dat;

analysis: type=mixture;

i s | y1@0 y2@1 y3@2 y4@3;
i* s*;
i with s*;
c#1 on x1*;



Output: tech9;
 Bengt O. Muthen posted on Tuesday, September 16, 2014 - 1:52 pm
A better method is Auxiliary x(R3STEP).

You should not include x in the modeling as you do when saying:

c#1 on x1*;
 Anna King posted on Tuesday, September 16, 2014 - 4:26 pm
Thanks, Professor! I did run the program but I couldn't find any regression coefficients for class and the covariate. Do you know how to get that? Thanks!
 Bengt O. Muthen posted on Tuesday, September 16, 2014 - 5:11 pm
Check that you are doing it correctly as described in Appendix A page 3 of the paper on our website:

Asparouhov, T. & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 329-341. The posted version corrects several typos in the published version. An earlier version of this paper was posted as web note 15. Appendices with Mplus scripts are available here.
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