Message/Author 

sharon su posted on Saturday, July 30, 2016  6:36 am



Hello ~ I am trying to analyze data using ex.7.27 syntax. 1. What's the difference if I choose continuous or categorical observed variables? Will the link function change from linear to logit one? 2. Do the two types have their approriate algorithm? 3. How can I detect the label switching phenomenon? Thanks a lot ~ 


1. Yes. 2. Yes. 3. Label switching is an issue with Bayesian estimation of mixtures, not ML  unless you are doing Monte Carlo studies over many replications. 

sharon su posted on Sunday, July 31, 2016  7:58 pm



1.When I try to figure out FMM with figures,I get stuck with the arrow from c to f. According to "latent variable hybrids", there's no arrow because the factor means can be standerized, but in Lubke & Muthen(2005, p.28), there does have an arrow from c to f. How can I explain the difference? Will standerized or not influence the model? 2. Would algorithm = intergration be suitable both for linear and logit situation? Thank you ~ 


1. c to f means that the means of f vary across the c classes. When c points to the indicators their means/thresholds vary across classes and therefore factor mean differences across classes cannot also be identified. 2. Yes. 

sharon su posted on Monday, August 08, 2016  5:02 am



Well, I generate two classes of data. The fisrt one with two factors mean (0,0), SD equals to 1, the second data with two factors mean (1.2,1.2). Then I combine the two data sets, and try to use ex 7.17 to analyze data. However, I can only get the item intercept deifference. What can I do to get the factor mean difference? Does the factor mean have to equal to zero for identification? 


The factor mean has to be zero in one class but should be free in the other class  just like is shown in 7.17. 

sharon su posted on Friday, September 02, 2016  1:58 am



I gnenerate a set of data with 2 factors, their covariance is about 0.25, factors loadings are 0.8. When I use FMM analysis dealing with the data, the general result output showed results as: F2 WITH F1 0.260 0.016 16.405 0.000 the STDYX result: F2 WITH F1 0.743 0.018 42.427 0.000 If I choose the STDYX result,why the covariance is not near the true value, while the unstanderdized result is? 


Your factor variances were probably not generated with variances one. 

sharon su posted on Friday, September 02, 2016  8:00 pm



I've checked the data, the variances were about 1, covariance about 0.25. MPLUS code: ANALYSIS: TYPE = MIXTURE; ALGORITHM = INTEGRATION; ITERATIONS = 1000; MODEL: %OVERALL% f1 BY i1i30; f2 BY i31i60; %c#1% [i1i60@0]; After rerun The model result F2 WITH F1 0.270 STDYX result F2 WITH F1 0.764 I am not sure what happpened? But the estimation value(correalation?) is far away from my data.Do I miss anything? 


To give you good advice I have to see your full outputs for both the datageneration part and the analysis part. Please send to Support along with your license number. 

sharon su posted on Monday, September 26, 2016  8:18 pm



I think it's because my data is binary response. When I use continuous indicators, the estimation result seems closer to the true value. Can I still use ex7.17 but change command to : VARIABLE:NAMES ARE u1u6; CATEGORICAL ARE u1u6; Would it be linear link funcion assumpution? Thanks ~ 


No, it doesn't have to do with binary response, When you declare a variable as Categorical, a probit or logit link function is used. 

sharon su posted on Tuesday, September 27, 2016  9:41 pm



You mean I can imitate ex 7.27? If I want to distinguish the probit(linear) from logit(nonlinear) link function ? How can I revise the command? Thanks ~ 


Link = probit; in the Analysis command. 

sharon su posted on Wednesday, September 28, 2016  11:00 pm



If I still use the same binary data, what's the difference between ex 7.17 and ex 7.27(link= probit)? I am kind of confused because they both seem to provide estimation result. Thanks ~ 


7.17 uses MLR and all maximumlikelihood estimators use Link=logit as the default. 

sharon su posted on Saturday, October 01, 2016  12:49 am



1. When facing ex 7.17 & ex 7.27, they both use logit link finction as default to deal with binary data, how can I choose between them? 2. Do the two examples both set constraint factor means = 0, factor variances =1 for identification? Do I have to give more restrictions? 3. When c were more than 2, is it enough to give constraint of foctor means = 0 and factor variances =1 for only one group? or both two groups need to have constraint? 


See my paper on our website: Muthén, B. (2008). Latent variable hybrids: Overview of old and new models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 124. Charlotte, NC: Information Age Publishing, Inc. Click here for information about the book. download paper contact author 

sharon su posted on Monday, November 14, 2016  4:19 am



If I want to have different resisual variances but have the same factor loadings on the two groups,how can I revise my code? Thanks ~ ANALYSIS: TYPE = MIXTURE; MODEL: %OVERALL% f1 BY i1i5; f2 BY i6i10; %c#1% [i1i10@0]; f1 with f2; %c#2% [i1i10@0]; f1 with f2; 


Your input gives equal loadings. To get groupvarying residual variances, just add i1i10; in both groups. I don't know why you fix the intercepts to zero. 

sharon su posted on Monday, November 14, 2016  9:51 pm



1. I change the code, only the model result shows equal factor loadings, but not in STANDARDIZED MODEL RESULTS, why? STDYX Standardization Latent Class 1 F1 BY I1 0.843 I2 0.797 I3 0.836 I4 0.797 I5 0.805 Latent Class 2 F1 BY I1 0.798 I2 0.780 I3 0.784 I4 0.793 I5 0.798 2. I give the assumption that the two groups have the same intercepts equal to zero when generating the data, so I give the constraint? 3. I generate the data with factor loadings 0.8, residual variances are 0.36, I choose the STDYX Standardization, is it appropriate? 


1. I assume it is because your variances are not equal across groups and they are used to standardize. 2. It is ok to generate by 0, but don't fix them to 0 in the analysis  you never have that knowledge in real data. 3. If you have a factor variance of 1 the total variance is 1 as you seem to assume here. 

sharon su posted on Monday, November 21, 2016  6:47 am



1. When I choose ANALYSIS: TYPE = MIXTURE to analyze data of two factors and two classes, why do I get different factor means in .out file and SAVE = FSCORES file. For example, in .out file for c1 and c2 f1 mean 0.768, f2 mean 0.736 (c2 are set to zero) of STDYX. However, in fscore file, I got the result for c1: f1 0.819523732, f2 0.802319149 for c2: f10.381434447, f2 0.362233933 I am not sure what happened? 2. I have results of F1 and C_F1. They are both eta results, what's the difference? Thanks ~ 


1. Estimated factor scores don't have the same means and variances as true scores. 2. F is the overall value, mixed over classes and C_F is the classspecific value. 

sharon su posted on Monday, December 05, 2016  11:43 pm



I am trying to find the more suitable factor mixture model for the simulated data. I have two assumptions: 1factor/1class¡B1factor/2class. However, in the 1factor/2class situation, person belonged to the second class were zero. The output still had different Loglikelihood and Information Criteria values compared with 1factor/1class. Why the estimation can be computed when one class were 0?and different with 1factor/1class? Thanks ~ 


We need to see the two outputs to answer your question  send to Support along with your license number. 

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