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I am running a 3 class 13 wave quadratic growth mixture model and have two questions. 1) The model converged and terminated normally, however, the variance estimate for intercepts for the 1st and 3rd classes were negative. This seems to indicate some problems with the model, what are your suggestions (this doesn't happen in the linear model, only in the quadratic)? 2) The standard errors for some of the estimates are quite large for the quadratic 3 class model (e.g., class 2 the variance and mean of the intercept). What might be the problem? Again this is not the case with the linear model. Thanks in advance. Moncia Oxford 

bmuthen posted on Wednesday, November 29, 2000  10:03 am



The fact that growth mixture modeling has more than one class tends to reduce the withinclass variation and in some cases it can be set at zero. You may not get a significant worsening of fit (e.g by likelihoodratio chisquare difference testing) if you fix the negative variance estimates at zero. If you do get a significant worsening of fit, this could indicate that the model is not appropriate for the data. If you have classspecific parameters, standard errors could be large due to small class sizes. The fact that your quadratic model seems to have more problems than the linear might point to the fact that you might only need a linear model once you allow several classes. A good way to vizualize your modeldata fit on an individual level is mentioned in the growth mixture modeling paper number 87 as listed on this web site. 


Thanks for the above response. I have one more question on the same model. I would like to add a time varying covariate, but, from what I understand, I can't run such a model with the current version of MPLUS, will this be a feature of version 2? 


Unless I am misunderstanding you, there is no reason that you cannot have timevarying covariates as part of your growth mixture model. They would be specified as y1 ON x1 and y2 ON x2, for example. Example 22.1c in the Mplus User's Guide contains timevarying and timeinvariant covariates in a growth model. 

Hemant Kher posted on Wednesday, October 27, 2010  2:30 pm



Hello Drs. Muthen and Muthen, I have a some questions on latent classes in a LGM. My sample has n=230 respondents. I have taken 4 repeated measurements on a continuous variable and fit a nonlinear LGM. 1) The LRT test (tech11 output) shows that 2class solution is significantly different from the 1 class solution. But, the two groups are quite apart in their sizes – 228 in one and only 2 in the other. In my readings I have not come across such a wide range and wonder about my results. The slope for the group with 2 students is almost 3.5 times that for the rest of the group, so I do see why the groups may be different. 2) The LRT test shows that 3class solution is significantly different from the 2 class solution, but, now the residual variance for time point 4 measure is negative (p=0.8). Can I set this to zero given that it is not significant? 3) Setting the negative residual variance for time point 4 to zero results in a 3class model with LRT that is significantly different from the 2class solution – can I assume that this comparison is against a 2class model where residual variance for time point 4 measure was also set to zero? 4) The LRT test shows that a 4class solution is not different from the 3class solution (p=0.0545). Can I conclude that my data contains 3 classes on the basis of these results? Thank you so much for your time; it is greatly appreciated. 


First, are you sure you are using enough starts? For example, try Starts = 400 100; Particularly with smallish sample sizes the mixture likelihood can be bumpy and in need of many starts. Do a first quick scan of the number of classes using BIC. I would not choose a solution with only 2 students in it. But, if those two students are so different, I would delete them and then do the GMM  they may obscure other interesting mixtures. The negative residual variance may point to the wrong functional form for the growth model  or that an outcome is very nonnormal (floor or ceiling effect). 


I've got an unconditional LGM (latent growth model) with three times measure and three classsolution. CSR is my outcome variable and all in this model work good. In the conditional model(with covariates like ROA and ROE among others financial indicators),the covariates are in the base line and all works too. But, how must I interpret the "R Square" section in output? For instance, I have 0.807 for the first time measure, 0.875 for first class intercept and 0.511 for slope. All of them are significant. Thanks in Advance 


The Rsquare for the outcome at the first time point is the variance explained in that outcome by the growth factors. I think by first class intercept and slope, you mean the intercept and slope growth factors. Rsquare for these is the variance explained by the covariates they are regressed on. See the Topic 3 course handout on the website around Slide 70. There is a path diagram of a conditional growth model that will make this clearer. 


Yes with intercept and slope I meaning the parametters of adjusted trayectories (It is the same you name factors). Then, the R squared has the same interpretation as multiple regression analysis and It is valid by standardized and nostandarized coefficients. The model estimates classes (unconditional) and regression (conditional) at a only one step. How can I obtain the uncondtional trayectories for each class and their equations? Thanks a lot in advance. 


Yes, the Rsquares have the same interpretation as in regular linear multiple regression. Not sure what you are asking here, but unconditional trajectories can be obtained by running a model without covariates. You can use the PLOT command to look at the estimated mean trajectories. 

Robert Vagi posted on Saturday, June 25, 2016  3:49 pm



I'm running a GMM with five time points and roughly 2,000 cases. The results suggest a three class solution with one class that is very small (roughly 5% of the population). My model fit looks good and my parameter estimates are theoretically appropriate, but I keep getting the following output for the residual variances of the very small group: "*********" I don't have any error messages and I can't find a reference to this output in the user's manual. What does this mean? Thank you in advance! 


Please send the output and your license number to support@statmodel.com. This means that the value is too large to fit in the space allocated for printing. The question is why. 

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