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Hi I have run the following code: DATA: FILE IS comb1log.dat; VARIABLE: NAMES ARE age t tdur liv x1x9; USEVARIABLES ARE t x1x9; CLASSES = c (4); Missing are all (999) ; CATEGORICAL = t; ANALYSIS: TYPE = MIXTURE; Type=Missing; STARTS = 20 2; estimator=mlr; algorithm = integration; Coverage=0.02; MODEL: %OVERALL% i s  x1@0 x2@1 x3@2 x4@3 x5@4 x6@5 x7@6 x8@7 x9@8; s on t; c#1 on t; c#2 on t; c#3 on t; But I have got difficulty in understanding the output  in particular, MPLUS outputs that: Categorical Latent Variables C#1 ON T 1.516 0.985 0.709 2.402 2.934 C#2 ON T 9.235 9.706 11.205 12.704 13.175 C#3 ON T 2.165 1.991 1.439 0.886 0.713 The above does no make sense to me, as classes 2 and 3 are virtually identical when looking at the graph (except that the intercept for 3 is slightly higher than for 2). Could you help me with the interpretation please? 

linda beck posted on Wednesday, January 14, 2009  3:39 am



I would say, if two of your classes look nearly identical you should prefer a 3class solution instead of 4 classes independently of what fit or test criteria say. May be that would help to get more plausible effects of c on t. 


yes, I did think that but even then I still have those two classes separating. It does make sense conceptually, but I am stuck on how to interpret the different effect of T on them. 


The results you show look odd. Please send your input, data, output, and license number to support@statmodel.com. 


Linda  thank you for the offer but unfortunately I am unable to send the data. I did, however, realise over the weekend that I should have specified T in dummy variables as it is a nominal categorical variable. That has solved the strange output problem. Still, I have got the following questions: 1) I am assuming that the model specification above does not estimate the effect of T on variability within classes? How would I specify that? 2) How do I specify that I would like the effect of s on T to be different across different classes? Thank you! 


1. i s ON t; 2. t ON s; 


thank you! unfortunately, when I use t on s I get the following fatal error: reciprocal interaction problem Is there anything else I need to change in the model? 


Please send your full output and license number to support@statmodel.com. 


That's an easy one I guess! I had the same problem some time ago. you should mention i on t in the %obverall%statement (if defined class invariant) and the class variant s on t in the classspecific statements (i think you have 4) for each class. it goes like this: %c#1% s on t; %c#2% s on t; ... The same procedure with other model parameters. Furthermore, I would increase the starts (you try to extract 4 classes!!!) up to at least 500 20, to avoid local maxima. stiterations = 20 also helps a lot to get more trustworthy solutions, especially when you have class variant effects. In addition, class variant effects can often lead (that's my experience) to LL's not replicated. Then stscale = 1 sometimes helps to replicate a LLvalue. your coverage seems pretty low, which can also lead to estimates that are not trustworthy. Is this what you intended? Tschuess, Michael! 


Michael  thank you very much for your help! Also, your pointers about the starts are greatly appreciated. Regards Sophie 


Good evening Dr. Muthen, I've run an LPA with 3 predictor variables and selected a 2cluster skew normal distribution model (unrestricted covariance matrix) as the optimal model. I'm having trouble understanding how to interpret the skew and df parameter output. A portion of my output looks something like this: Skew and Df Parameters Latent Class 1 Predictor 1 16.991 12.200 1.393 0.164 Predictor 2 14.354 5.769 2.488 0.013 Predictor 3 27.466 7.234 3.797 0.000 How do I know what the skew value is for each variable? 


Send your full output to Support along with your license number. 

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