Bayesian LCA - Assigning Individuals ... PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Chris Koenig posted on Wednesday, March 27, 2013 - 2:28 am
Dear all,

I am conducting a Bayesian LCA with three indicators and two distal outcomes. Since MODEL TEST and the AUXILIARY approach are not available for ESTIMATOR = BAYES, I use the "old" approach and look at the variation of the outcomes across classes.

The model runs fine, the chains are mixing good and converging, and I get a reasonable class-solution. However, I am stuck at assigning individuals to classes. I do not know how to assign individuals to classes; with ESTIMATOR = ML, you just use SAVE = CPROB to obtain most likely class-membership. With ESTIMATOR = BAYES, this option is not available. When I use SAVE = FSCORES, the plausible values for most likely class membership never match the number of individuals in the respective groups.

I feel like I am missing an obvious point here, but could somebody kindly tell me how (i.e. based on what output/part of the output) to assign individuals to classes in Bayesian LCA?

Thank you so much!

Kind regards,
 Bengt O. Muthen posted on Wednesday, March 27, 2013 - 3:36 pm
Please send your data, input, output, and save file to Support so we can help you.
 Dmitriy Poznyak posted on Friday, December 15, 2017 - 12:36 pm
Hi, Mplus team,

I am stuck with the same question as the topic-starter. For several reasons (skewed distribution of the variables; time it takes to run ML and convergence issues in ML and large N, among the rest) we chose to run a LPA model with the Bayesian estimator. We use Mplus version 7.2.

However, we are not able to save the individual class membership in the separate file. We get a warning that "Class probabilities cannot be saved for ESTIMATOR=BAYES." We'd appreciate your suggestions!

Also, perhaps you can point me in the right direction with another question. We'd like to define/predict the cut-points (for continuous class indicators) that can be used as data-driven threshold to define the classes. For example, we would like to say that an observed score K on a continuous indicator X can be used as a reasonable "threshold" (defined in a non-tech way) between classes 1 and 2. Predicted means get us half-way there, but I think there may be a better approach that would account for the beneficiaries below/above the mean in each class. I wonder if you've come across a similar issue.

 Tihomir Asparouhov posted on Friday, December 15, 2017 - 2:41 pm
The command to save the class probability is

savedata: file=1.dat; save=fs(100);

On the second question I would recommend using K where




K=m1-t*s1=m2+t*s2, => t=(m1-m2)/(s1+s2)


 Dmitriy Poznyak posted on Monday, December 18, 2017 - 9:47 am
Hi Tihomir,

I appreciate your prompt response. Super helpful, as usual. Re: my second question. In your example above. does S1/S2 refer to the S.D. of the mean within a class? If so, is this information a part of the Mplus LPA output? I know Mplus provides S.D. of the estimate (posterior S.D. in our case) but this is different.

Also, for a LPA model with more than 2 classes, would we basically repeat this approach for Class 2 and Class 3, etc.?

Thank you,
 Tihomir Asparouhov posted on Tuesday, December 19, 2017 - 10:18 am
S1 and S2 are the standard deviations for the variable Y in the two classes, i.e., S1*S1 and S2*S2 are the variances of the class indicator Y in the two classes (it is not the SD of the mean estimate).

For more than two classes you should be able to repeat that process.
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