Message/Author 

Qi posted on Tuesday, June 02, 2009  2:46 pm



Dr. Muthen, In a GMM analysis, I am trying to decide whether to use a linear or a quadratic growth model, I am wondering if there are some guidelines for how to approach this. Shall I 1) fit a regular growth model(with 1class) first, and decide to include the quadratic term or not (I assume using the chisquare test for nested model), and then fit the GMMs to decide the # of classes; or 2) decide the # of classes in GMM, and then decide if I should include quadratic terms? It's hard to know if each latent class would need the quadratic term though. Your guidance would be greatly appreciated! Qi 


You should include a quadratic growth model in the overall part of the MODEL command. You can then look at the means of the quadratic growth factor in each class and see if it is different from zero. 


Related to the above discussion, we have estimated an unconditional growth curve that demonstrates better fit with quadratic than linear model. As a next step we looked at a 5 class latent class growth model. In this model, the three most substantively interesting classes have nonsignificant quadratic means. Additonally, the slopes for these 3 classes are also nonsignificant, but eyeballing their graphs it would appear there is a significant slope. I'm wondering if the incorrect inclusion of a quadratic term for these 3 classes could be affecting the significance of the slope mean estimate? The two other classes, which have a higher N (ex.stable low class) do however, demonstrate a significant quadratic mean. In this case, do you suggest maintaining the same quadratic or linear model for all classes? Or is it possbile/correct to specify different models (linear vs quadratic) for the different classes? If this is the case, is there a reference you are aware of for how to go about settnig this up? Thanks so much 


Without substantive theory to the contrary, it seems like a reasonable approach to use the same functional form for all classes and then report where some parameters are insignificant. You get a separate form simply by specifying a different growth model within the class in question. 

HanJung Ko posted on Wednesday, April 02, 2014  11:56 am



Dr. Muthen, I am working on a growth mixture model with outcomes of 5wave continuous variables. Before proceeding to class analysis, I wonder how I could determine whether it should be better estimated as a linear or quadratic model? 1. The estimated means of slopes in the linear model were not significant, nor were the slopes and quadratic terms in the quadratic model. 2. Would it be legitimate if I determine whether linear or quadratic estimations are more appropriate by Chisquare tests? If so, where should I look for the values in the output? ChiSquare Test of Model Fit or ChiSquare Test of Model Fit for the Baseline Model? What does it mean if the ChiSquare Test of Model Fit is not significant? I read through example 6.1 and 6.9 but I am still not sure how to compare the linear and quadratic models. Thank you, HanJung Ko 


I think it is sufficient to look at the z scores for the linear and quadratic growth factor means. So it sounds like you have an intercept only growth model. 

HanJung Ko posted on Monday, April 07, 2014  11:58 am



Thank you, Dr. Muthen. I figured keeping the linear model is more reasonable. Another question encountered is when I tried to apply the 3step approach to estimate the model. I was able to estimate the classes in step 1 (2 classes identified) but failed in step 2, where I have a few categorical and continuous variables to predict the probabilities of being in each class. Analysis: Type = Mixture; Starts = 500 80; LRTSTARTS = 1000 1000 1000 1000; Stiterations = 50; processor = 4; Model: %OVERALL% !i s  pl_1@0 pl_2@1 pl_3@2 pl_4@3 pl_5@4; !i WITH s; c on sex AA total_t1lgs total_t1neo_c total_t1neo_n total_t1neo_e; %c#1% pl_4; [n#1@1.955]; %c#2% pl_4; [n#1@3.150]; My reference: http://www.statmodel.com/download/AppendicesOct28.pdf But I think the difference is that I did not have auxiliary variables in my model but only predictors for class in step 3. Thank you! I really appreciate your help! HanJung Ko 


I don't know what the problem is that you describe. Why do you say "pl_4" in the classspecific part of the model? 


Sorry I did not describe my question well. Based on Asparouhvo & Muthen (2013), my step 1 model is: Usevariables are pl_1 pl_2 pl_3 pl_4 pl_5; Missing are all(9999) ; Classes=c(2); SAVEDATA: FILE = gmm2step.dat; save = cprobabilities; Analysis: Type = Mixture; Model: %OVERALL% i s  pl_1@0 pl_2@1 pl_3@2 pl_4@3 pl_5@4; i WITH s; The problem was when I proceed to estimate whether gender, race, personality traits etc. predict the class membership, Mplus could not read the data saved from step 1. Model: %OVERALL% c on sex AA total_t1lgs total_t1neo_c total_t1neo_n total_t1neo_e; %c#1% [n#1@1.955]; %c#2% [n#1@3.150]; I guess it is because in the data from step 1, there are only estimated PIL1~5, intercept mean and Std., slope mean and Std., p(1), p(2), and n. There are no data on gender, race, and personality traits etc. I did try add: auxiliary= AA sex total_t1lgs total_t1neo_c total_t1neo_n total_t1neo_e; But the model did not work. Thank you for the help in advance. HanJung Ko 


Send the output from your problematic run and license number to Support. 

Yue Liao posted on Thursday, June 26, 2014  12:36 am



Hi Dr. Muthen, I have a 3class GMM using a zeroinflated Poisson model. I want to have one class as linear, and the other two as quadratic. How do I specify that? Here is my model: %overall% i s q  smk1@0 smk2@.1 smk3@.2 smk4@.3 smk5@.4; ii si qi  smk1#1@0 smk2#1@.1 smk3#1@.2 smk4#1@.3 smk5#1@.4; sqi@0; How should I specify class 1 so it will be linear? 


One comment. You do not have a GMM if the variances of the growth factors are fixed at zero. To have different growth models in different classes, specify the most complex model in %OVERALL%. In the other classes, fix the means, variances, and covariances of the growth factors you don't want to zero. 

Yue Liao posted on Thursday, June 26, 2014  3:45 pm



Hi Linda, I followed Example 8.5 to set up my model. I thought it's a GMM? If I don't want class 1 to have the quadratic term for the growth model, do I just put: %c#1% q@0; or %c#1% i s; Also, if I want to have a cubic function, is the following code correct? %overall% i s q c  smk1@0 smk2@.1 smk3@.2 smk4@.3 smk5@.4; ii si qi ci  smk1#1@0 smk2#1@.1 smk3#1@.2 smk4#1@.3 smk5#1@.4; I want to start with a simple model (i.e., a fixed effect model), so should I put sci@0; Thanks a lot! 


I see. There is one growth factor free. The reason the example does this is because each growth factor requires one dimension of integration. The variance and covariances are zero. To set the mean to zero say %c#1% [q@0]; Yes, that is correct for a cubic model. 

Yue Liao posted on Friday, June 27, 2014  1:28 pm



Thanks Linda! Also, our measurement points are one year part, I assume smk1@0 smk2@.1 smk3@.2 smk4@.3 smk5@.4 would be the same as smk1@0 smk2@1 smk3@2 smk4@3 smk5@4; correct? Another question. ANALYSIS: TYPE=MIXTURE; STARTS = 5000 50; STITERATIONS = 100; ALGORITHM = INTEGRATION; ESTIMATOR = MLF; For the above spec, the model still says "THE SOLUTION MAY NOT BE TRUSTWORTHY DUE TO LOCAL MAXIMA. INCREASE THE NUMBER OF RANDOM STARTS." Should I then change the algorithm to MONTECARLO (suggested by the manual since random starts already reach 5000)? And will increasing STITERATIONS and/or integration number make a difference at this point? 


Answers to your 3 questions: Q1. Yes. Q2. Not necessarily. Increase the number of random starts using STARTS= if the best loglikelihood value is not replicated several times. Q3. Only if your problem requires numerical integration and TECH8 shows negative "ABS" changes. 

Yue Liao posted on Monday, June 30, 2014  1:22 pm



So to confirm, if the problem is only about "the best log likelihood value is not replicated", I can just keep increasing the STARTS number, like, from 5k to 10k or more. Meanwhile, I don't need to increase STITERATIONS unless I see negative "ABS" changes. What about the 2nd part of the STARTS (# of optimizations in the final stage)? At what point I'll need to increase it? Thanks a lot!! 


I recommend using Starts = y x; where x is about 1/4 of y. So you want to increase both in tandem. I always use the default Stiterations and it does not have to do with negative ABS changes. The problem of neg ABS changes has to do with lack of numerical precision with numerical integration, in which case you want to increase the number of integration points. 

Eva Guerin posted on Wednesday, June 08, 2016  3:13 pm



hi there, Naive question again  sorry of this is repetitive of previous discussions. Adding a quadratic growth term to a 3class solution (whether it is added to all classes or just one) changes class membership/cprobs... Is this normal? Is it best to use the start values from a simpler model with just the linear growth factors in order to test the significance of the quadratic growth factor in my already established classes? I am trying to compare models with linear vs. linear+quadratic vs. quadratic growth for my trajectories are better  but logically, I would think that having the same class membership for the comparison would make the most sense. Am I way off? 


Q1. Yes, because it changes the model. Q2. No. Instead, use either BIC. 


Hi, I am not sure, if i should use a quadratic or a cubic growth model in my GMM. My dataset includes around 500 patients and 9 timepoints. I read in your reply above, "You should include a quadratic growth model in the overall part of the MODEL command. You can then look at the means of the quadratic growth factor in each class and see if it is different from zero." But my questions are: 1) Is the same true for a cubic model? 2) In which technical output should I look for the means? 3) What if the means are 0 (or really close to 0, one was for example 0,001) but the P value is still significant? Kind regards, Ida BehrendtMøller 


The simplest approach is to use BIC. The means are found in the regular output. 

Cherry Chu posted on Saturday, November 04, 2017  7:11 am



Hi, I am conducting GMM and trying to decide on the number of classes as well as linear/quadratic/cubic. I was wondering if the following approach is reasonable: 1. Determine the number of classes via BIC, using the linear growth model 2. Using the xclass model found in step 1, determine linear vs quadratic vs cubic via BIC, by adding additional growth factors sequentially. Thank you. 


No, you should do it jointly because different growth functions may have the best BIC at different number of classes. 


I am testing a GMM with latent classes, and i want to test for cubic curvature. However, when I use "c" in my model statement, i get the error message that growth factors cannot have the same name as latent variables. How can i desigate cubic in my model line? Thank you. 


You can use any name  it is deemed cubic when it is the 4th entry. 


Thank you for your quick response. Second question.... is it possible to test quartic curvature? I haven't seen this in any examples, but since I have six time points, it is theoretically possible. But when I add another term to my model statement, it doesn't seem to like it. 


The automatic approach only does i s q c, but you can instead use BY to have any curve. 


Hello, I want to conduct a LGM with a variable assessed in 5 occasions. I have 3 questions: I1. have tried with linear and quadratic and it seems that my curve fits the data better with quadratic term, although the quadratic term itself is not significant. is this possible at all? 2. I next want to run a mixture model. shall i use the model above that fits the data better as baseline? and then look at each class separately if necessary? 3. I want to include covariates to improve the model prediction  it is better to do so in the mixture part or at the beginning when I am estimating the baseline model?. Thanks!! Borja 


1. I don't know if you refer to the mean or variance of the quadratic. Often the variance is not significant but the mean is and that is a useful model. 2. A good start is to use the same growth function as in a singleclass run. 3. I'd do it at baseline. 


Dear Prof. Beng: I refer to the mean not being significant. It is still useful if the fit is better than with the lienar term? Thanks for your feedbck! Borja 


If the mean is not significant, the variance has to be significant for the quadratic to be useful. That seems like a rare occurrence, however. 


sorry forgot to mention that variance of the quadratic term s significant. Thanks 

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