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 Michael posted on Monday, November 28, 2005 - 6:19 pm
Iím running a GMM model with 3 time points similar to Example 8.6 of the Userís Guide. Iím having difficulty in specifying the correct model command to achieve the relation to the binary distal outcome (DisOut).

The Userís Guide indicates that ďÖsince automatic starting values are used, it is not necessary to include class-specific statements in the model command.Ē (pg 163).

Iím specifying starting values for a 3 class solution and have a time varying covariate (TVC) at each measure. I also have antecedent variables (time invariant, x) predicting class membership.

Iím unsure as to what the model command would be for the distal outcome (DisOut).

%Overall%
i s | y1@0 y2@1 y3@2;
i s ON x;
c#1-c#2 ON x;
y1 ON TVC1;
y2 ON TVC2;
y3 ON TVC3;

%c#1%

[i*3 s*5];
y1 ON TVC1;
y2 ON TVC2;
y3 ON TVC3;

%c#2%

[i*1 s*.5];
y1 ON TVC1;
y2 ON TVC2;
y3 ON TVC3;

%c#3%

[i*1 s*-1];
y1 ON TVC1;
y2 ON TVC2;
y3 ON TVC3;
 Linda K. Muthen posted on Monday, November 28, 2005 - 8:57 pm
You refer to the distal outcome by its threshold within the %c#% statements. By default these are different across classes which implies that c influences the distal.
 Tracie Barnett posted on Monday, August 25, 2008 - 10:49 am
Hi,
I'm a new user and have been going through your video courses. In your LSAY example with high school dropout as a distal outcome, could you tell me how you computed the probability of outcome (e.g. 69% in class 1, the low math achievers) given the output? I'm not sure which part of the output informs us about this; the only thing I see relating the distal to the classes is the threshold.
Many thanks,
Tracie
 Bengt O. Muthen posted on Monday, August 25, 2008 - 2:29 pm
You get thresholds for each class and the thresholds can be translated into probabilities. See slide 131 of Topic 6 - the alcohol example shows how to compute the probabilities from the thresholds.
 Tracie Barnett posted on Monday, August 25, 2008 - 2:55 pm
Thank you,
that's very helpful!
Tracie
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