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Hi I have run the following code: DATA: FILE IS comb1log.dat; VARIABLE: NAMES ARE age t tdur liv x1-x9; USEVARIABLES ARE t x1-x9; 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? |
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linda beck posted on Wednesday, January 14, 2009 - 3:39 am
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I would say, if two of your classes look nearly identical you should prefer a 3-class 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. |
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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. |
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The results you show look odd. Please send your input, data, output, and license number to support@statmodel.com. |
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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! |
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1. i s ON t; 2. t ON s; |
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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? |
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Please send your full output and license number to support@statmodel.com. |
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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 class-specific 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 LL-value. your coverage seems pretty low, which can also lead to estimates that are not trustworthy. Is this what you intended? Tschuess, Michael! |
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Michael - thank you very much for your help! Also, your pointers about the starts are greatly appreciated. Regards Sophie |
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Good evening Dr. Muthen, I've run an LPA with 3 predictor variables and selected a 2-cluster 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? |
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Send your full output to Support along with your license number. |
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