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?
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.
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.?
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.