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?
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.
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?
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 i1-i30; f2 BY i31-i60; %c#1% [i1-i60@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 data-generation 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 u1-u6; CATEGORICAL ARE u1-u6; Would it be linear link funcion assumpution? Thanks ~
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. 1-24. 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 i1-i5; f2 BY i6-i10; %c#1% [i1-i10@0]; f1 with f2;
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: f1-0.381434447, 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 class-specific 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 ~