I'm trying to implement the Satorra-Bentler strictly positive chi-square difference test as described in Mplus Web note 12 for a factor mixture model with 4 classes. I have followed the procedure outlined in the web note except that to prevent Mplus from updating the start values, I have set convergence, mconvergence, logcriteria and rlogcriteria parameters to large values. Despite all this, Mplus takes a 2nd iteration in the EM algorithm regardless of how high I set the convergence parameters. If I put in start values with more then 3 significant digits, like 6 significant digits, which is considerably more work then using the svalues feature, Mplus still takes a 2nd iteration even though almost nothing changes. Is there something else that has to be set?
I tried setting miter=1 and then Mplus tells me that an insufficient number of E steps have been taken and I don't get the MLR scaling factor that I need to compute the strictly positive test. I will send you the example and data.
I am running into the same problem as presented above with estimating an m10 model. I have tried increasing the convergence, but there are iterations in the 'gradient' and 'quasi-newton' sections. Is there any more news on this issue? Thank you.
I am also running into this same problem with type=random and algorithm=integration. When I set miter=1, it tells me "THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN INSUFFICIENT NUMBER OF E STEPS. INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED" and it does not give me any loglikelihood or scaling factors.
Was there a resolution to this problem that would allow me to get the scaling factor for the M10 model?
Dear Muthéns, I performed a LMS model with 2 groups (type=mixture random; algorithm=integration). Subsequently, I computed a SB scaled Chi square difference test to compare a model without the interaction term (Model 0) to a model with the interaction term (Model 1). Thus the result of the SB scaled Chi square difference test was negative, I tried to estimate a third model (Model M10). I followed web note 12, example 1. My models are: M0 – Model without interaction term, output: svalues; M1 – Model with interaction term in each group; M10 – 1) Model produced by svalues; 2) adding the interaction term in each group (f3 on f1xf2) Question: Is this the right procedure for LMS models? I am asking because the scaling correction factor of M10 is smaller than the scaling correction factor of the M1 model – thus the chi-square is still negative. Mplus is fixing the interaction term automatically to zero. Thank you very much.
I'm also having a hard time figuring out where my strictly-positive chi-square difference test is going wrong. I'm doing an LCA with KNOWNCLASS , so my restricted M0 model is constraining the parameters to be equal between the two known classes (sex), while the M1 model is allowing the parameters of the 2 latent classes to differ by the KNOWNCLASS. I have tried using the starting values from the M0 output and setting CONVERGENCE=1000000000; MITER=1; or both. The resulting models either take iterations in the gradient or quasi-newton sections or fail to converge (INSUFFICIENT NUMBER OF E STEPS). Any help would be appreciated.
Thank you very much for that suggestion. I found that making that change fixed the problem with the analysis taking iterations, but some other problems emerged. I was unable to get Mplus to estimate more parameters when I cut and pasted the starting values from the M0 output where parameters were constrained between the two knownclasses (sex). I ended up removing the starting values from the 2nd knownclass (i.e., the G#2.C#1 and G#2.C#2 specific statements) to fix this problem. This produced a saddle point error message in estimation (WARNING: THE MODEL ESTIMATION HAS REACHED A SADDLE POINT OR A POINT WHERE THE OBSERVED AND THE EXPECTED INFORMATION MATRICES DO NOT MATCH. AN ADJUSTMENT TO THE ESTIMATION OF THE INFORMATION MATRIX HAS BEEN MADE.) It did estimate the correct number of parameters and examination of the latent class patterns showed that thresholds differed. The computation of chidiff using this LL1 and cd value seemed to be feasible, but I wonder if taking the additional step of removing the starting values for the G2 knownclass invalidates or defeats the purpose of using STARTS=0 and taking other steps to ensure that the M10 model is correctly specified?
If I am not mistaken, in the M10 model you need to remove the parameters constrains between the two knownclasses. If the log-likelihood value for the M10 model is not the same as the log-likelihood value for the M0 model the procedure is not valid.