Limiting the latent classes in a know... PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
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 Donald Compton posted on Wednesday, July 19, 2006 - 4:49 pm
I have the following code for a dataset I am running.

VARIABLE: NAMES ARE ID GP Y1-Y18 U1-U3 MFS OVS AMS WVS LCS OV RLN SM WV
LC RDN;
USEVARIABLES ARE Y1-Y18 U1-U3 OV SM RDN;
CLASSES = cg (4) c(2);
KNOWNCLASS = cg (GP = 1 GP = 2 GP = 4 GP = 5);
CATEGORICAL = U1-U3;
CENTERING = GRANDMEAN (OV SM RDN);
MISSING IS BLANK;
AUXILIARY = ID;
ANALYSIS: TYPE=MIXTURE MISSING;
ESTIMATOR=MLR;
STARTS = 20 2;
MODEL: %OVERALL%
i s q| Y1@0 Y2@1 Y3@2 Y4@3 Y5@4 Y6@5 Y7@6 Y8@7 Y9@8
Y10@9 Y11@10 Y12@11 Y13@12 Y14@13 Y15@14 Y16@15
Y17@16 Y18@17;
i s q ON OV SM RDN;
f BY U1-U3@1;
c#1 ON cg#1 OV SM RDN;
c#1 ON cg#2 OV SM RDN;
c#1 ON cg#3 OV SM RDN;

My question is whether there is a way to specify just 1 latent class in cg#3?
Thanks,
Don
 Linda K. Muthen posted on Wednesday, July 19, 2006 - 6:39 pm
cg#3 refers to class 3 of the categorical latent variable cg. If you want the categorical latent variable cg to have one class, you would specify

CLASSES = cg (1);
 Donald Compton posted on Thursday, July 20, 2006 - 8:53 am
Yes I understand that, however wouldn't that set the number of classes to 1 in all known groups? What I was hoping to achieve was cg#1(2), cg#2(2), cg#3(1), and cg#4(2). Can this be done within the classes command?
Thanks,
Don
 Linda K. Muthen posted on Thursday, July 20, 2006 - 2:55 pm
I think what you are asking is whether a known class can have a different number of classes on the other categorical latent variable. I think you would specify this as follows:

MODEL:
%OVERALL%
c#2 ON cg#3@-15;
 Philippa Clarke posted on Tuesday, June 22, 2010 - 1:30 pm
Apologies for the simplicity of this question, but I just need to clarify....

I am running a growth mixture model with known classes (multiple group analysis), using a binary dependent variable U.

In the output there is a threshold value (identical across groups), as well as an intercept and slope that are unique to each group.

The probability of U = 1/(1+e-(-threshold + intercept + slope*time))

Logit = -threshold + slope*time

Correct?
 Bengt O. Muthen posted on Tuesday, June 22, 2010 - 5:58 pm
The logit is your argument:

-threshold + intercept + slope*time

Note that this gives the probability conditional on these intercept and slope values.
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