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I guess you are using interchangeably the concept of components- a term in finite mixture models; and subgroup- a word coined by yourself and may be more suitable for your analysis. PROBMODEL Statement :: SAS/STAT(R) 14.1 User's Guide says that the PROBMODEL statement is used for building regression models (usually logistic regression) for component membership. Odds ratios and other common metrics reported in logistic regression can be calculated from the table named "Parameter Estimates for Mixing Probabilities" in the output. ... View more

No, you are interpreting it correctly.  I was wrong about what was contained in that data set. In any regard, hypothesis testing in finite mixture models is not very well defined because it is difficult if not impossible to derive the asymptotic distribution for the mixture likelihood.  There is a paper that discusses the problem and makes a few suggestions related to goodness of fit tests that might work.  Anything that they propose would not be available in SAS, but you might be able to program it yourself. Hypothesis testing for finite mixture models - ScienceDirect   ... View more

I think you are on the right track with that documentation link, I would recommend you re-read that example very closely. To summarize what is in the documentation, the binomial cluster model you are fitting is a two-component mixture, where the first component is binomial with 'n' trials and success probability 'mu_star + mu', the second component is binomial with 'n' trials and success probability 'mu_star', and the mixing probabilities are represented by pi and (1 - pi). Furthermore, mu_star = (1 - mu)*pi (where pi is still the mixing probability).   They show in the linked example that the estimate for the mu parameter, 'mu_hat', is computed as the inverse link of the intercept parameter in the model for mu (specified via the model statement). In the documentation example, mu_hat is equal to 0.5831. Likewise, in Table 43.11, they show how to compute estimates for the mixing parameter ('pi_hat') based on the coefficients from the model specified in the probmodel statement.    I believe that the pred statement for this model is generating the success probabilities for each of the two components of the mixture model, i.e., pred1 is mu_star_hat + mu_hat, and pred2 is mu_star hat. So, for example, for PHT = 0 and TCPO = 0, pi_hat = 0.6546, therefore mu_star_hat is (1 - 0.5831)*0.6546 = 0.273 (rounded), and mu_star_hat + mu_hat = 0.5831 + 0.273 = 0.865. Those are the values of pred_2 and pred_1 that I get, respectively, when PHT = 0 and TCPO = 0.     ... View more

This is exactly what I was looking for. Thank you! ... View more

This is helpful, thanks! In my case, I think the easiest way to go will be to adjust the components after running the model in a post-processing step. I appreciate your response.  ... View more