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Hi! I have ~35 days of zeroinflated substance use data and want to disaggregate between and withinperson processes with a twopart model. I'm using RDSEM and including time trends for both the binary and continuous parts. I'm wondering... 1. Is it reasonable to have time trends for the zeropart and continuous part, and separate autoregressive parameters for both? I'm concerned I'm missing something, given Dr. Muthen's choice to model the autoregression with a single latent variable at the Johns Hopkins Short Course and his statement that twopart DSEM isn't quite there yet. 2. I read... somewhere... that RDSEM is not available for categorical data, but I'm able to estimate autoregressions for the residual of the zeropart of my outcome as long as I keep it as a fixed effect. Am I missing something? 3. None of my twopart models provide a DIC or any other fit criteria. Is there any empirical way to determine whether effects should be fixed or random? Thanks so much! 


1. Yes, this is reasonable. We have moved the Mplus technology for 2part further since the Hopkins presentation in August 2017  now you don't have to work with that factor. 2. Again, the Mplus technology has moved forward  see Version 8.2 developments on the Version History page at http://statmodel.com/verhistory.shtml 3. Only by checking the magnitude of the variance. 


Thanks so much for your response! Two quick followup questions: 1. My model runs in DSEM, but the same model in RDSEM gets the error "this model is not available. Variables regressed on lagged variables should also be lagged." The error is driven by the binary part. My withinperson statement looks like this: y^ on y^1; u^ on u^1; m1^ on m1^1; m2^ on m2^1; y on m1 m2; u on m1 m2; ! commenting out this line makes the model run Why is this a problem for the binarypart but not the continuous part? It seems consistent with other RDSEM code from papers/the website... 2. When I remove the offending lines, estimation blows up at the first iteration because the betweenlevel posterior covariance matrix is NPD. I don't have this problem in DSEM, but I know from the appendix of Asparouhov et al. (2018) that DSEM will get point estimates correct but not variances, and so I wonder whether DSEM is overestimating the variances. Is this an indicator that I should simplify the model, or is there a better solution? (I'm running version 8.2). Thanks again! Matt 


1. The only thing that I can think of is that some of the variables have lag bigger than 1. All variables must have the same lag for this model. I would recommend using 8.3 and if that doesn't fix the problem send it to support@statmodel.com. I recall some fixes regarding similar situations were done in 8.3 2. This problem is most likely data specific. To diagnose it make sure the twolevel model without any autocorrelations is running ok. Also very small data sets could cause such a problem due to some autocrrelations >1. Also try this model first y^ on y^1; u^ on u^1; y on m1 m2; u on m1 m2; Again if unable to move forward send it to support@statmodel.com 

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