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I am estimating a crosslagged model on panel data where I have an X and a Y measured at 3 occasions. X is a categorical (binary) variable, Y is a count variable. So I have: x3 on x2 y2; y3 on x2 y2; x2 on x1 y1; y2 on x1 y1; For this type of model, it is important to model covariances between disturbance terms of endogenous variables. so I want to estimate: x3 with y3; x2 with y2; x3 with x2; y3 with y2; however, even using the theta parameterization, I cannot seem to estimate the variance of the disturbance term of the count variables (y1y3). How should I deal with this? Thank you for your help, Patrick 


Mplus does not include residual variances for Poisson regression with a dependent variable that is a count variable  this is in line with regular Poisson regression analysis where such a residual is absent. The Theta parameterization is not in effect for count outcomes. You can, however, in Mplus define a residual by defining a factor that influences the dependent count variable, e.g. when regressing y on a covariate x, f by y@0; y on x f@1; which would estimate a residual variance in the form of the factor variance for f. With say 2 dependent variables that are counts, you can hereby correlate the residuals across the 2 regressions. 


Thank you Bengt do you have a reference for this approach? Patrick 


This is in line with approaches used in the Hidden Markov references given in the Mplus User's Guide: Mooijaart (1998) and Langeheine & van de Pol(2002). 


Bengt so I would use theta paramterization to deal with the binary variables and not specify the count variables as counts in this specification? Patrick 


Sorry, somehow my answer above ended up in the wrong thread  we were talking Poisson modeling, not latent class modeling. So you would treat the outcomes as a blend of categorical and counts and use ML estimation (no other choice with counts). Note that you would only covary 2 sets of residuals, x3 with y3; x2 with y2; You have regression relations for the other 2 that you listed. With ML estimation you don't have access to Theta, so you have to bring in a dummy factor. Take for example the first residual covariance of x3 with y3: fy3 by y3@0; fy3@1; fx3 by x3@0; fx3@1; y3 on fy3@1; x3 on fx3@1; fy3 with fx3; The last statement gives you the residual covariance you wanted. I am not sure if it is identified, but you can try. No reference for this; just utilizing Mplus. 


thanks for the clarification. One (hopefully) final thing  do I specify the observed count variables as counts in this specification? i.e.: count are Y3 Y2; 


That's right. 


I am estimating a parallel process latent growth model with four outcomes, over three timepoints. Three outcomes are continuous and the fourth is a count of conduct problems (from 0 to 7). This is my input: USEVAR are TDer3 TDer4 TDer5 TDef3 TDef4 TDef5 dep3 dep4 dep5 cdc73 cdc74 cdc75; count are cdc73 cdc74 cdc75 ; Analysis: Algorithm = integration ; Integration =montecarlo(5000) ; MODEL: ider sder  TDer3@0 TDer4@2.8 TDer5@4.6 ; idef sdef  TDef3@0 TDef4@2.8 TDef5@4.6 ; idep sdep  dep3@0 dep4@2.8 dep5@4.6 ; icond scond  cdc73@0 cdc74@2.8 cdc75@4.6 ; I want to covary the residual variances for the observed variables. As an experiment I tried the approach outlined above for cdc73 and dep3, and it ran: fcd3dep3 by cdc73@1 dep3 ; fcd3dep3@1 ; [fcd3dep3@0] ; However, I would need nine residual covariances in total and each of these adds a dimension of integration. Is there an alternative method that would be faster? 


You could try using only 1 factor for each of the 3 time points and let the different loadings capture the different sizes of the residual correlations among the 3 processes. So 3 dimensions instead of 9. Make sure the 3 factors are uncorrelated with each other. 


Thanks very much  would the factors be defined like this? f3 by cdc73@1 dep3 TDef3 TDer3 ; f4 by cdc74@1 dep4 TDef4 TDer4 ; f5 by cdc75@1 dep5 TDef5 TDer5 ; f3@1 ; [f3@0] ; f4@1 ; [f4@0] ; f5@1 ; [f5@0] ; f3 with f4@0 ; f3 with f5@0 ; f4 with f5@0 ; 


Yes, but those 3 factors need to also be set uncorrelated with your 8 growth factors. 


Good morning, I am running a negative binomial model with 2 count predictors, 2 continuous mediators, 2 count outcomes (y1, y2), and 1 dichotomous covariate. I tried to estimate the residual covariance between y1 and y2 (study hypotheses expect significant covariance) by defining a factor that influences both outcomes: fy2 by y2@0; fy2@1; fy1 by y1@0; fy1@1; y2 on fy2@1; y1 on fy1@1; fy2 with fy1; However, there were several issues: (a) estimated residual covariance was very high (.96), (b) nontrustworthy standard error estimates and fixing several parameters to avoid singularity of the information matrix, and (c) significant indirect effects emerged that were not present before defining the residual. Is this the correct way to estimate the residual covariance? Are these indirect effects interpretable? Or, is it not appropriate to estimate the residual covariance in this model? Thanks, 


You may get the same results, but try: f by y1 y2; f@1; Then the y2 loading picks up the covariance. If this doesn't help, you can send output to Support along with your license number. 

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