Iím using latent class regression on a 2-class solution with 6 indicators. For the regression, I am fixing the conditional probabilities for the indicator thresholds at the starting values and entering groups of covariates in the OVERALL statement using C#1 ON X1 X2 etc. I get the following error: ONE OR MORE MULTINOMIAL LOGIT PARAMETERS WERE FIXED TO AVOID SINGULARITY Ö The odd thing is that when I enter the first four covariates, all regression coefficients are fixed. In the second group (now 9 total), all are again fixed. However, when I enter the last two and a new parameter from Model Constraint: New, all 12 parameters are estimated. Since Iím fixing the latent class indicator thresholds, the classes donít change in makeup, only in size (78 obs. are dropped in the first LCR and 120 are dropped in the 2nd and 3rd). I canít figure out why adding more covariates would make the coefficients estimable.
Sorry; I should've been more clear--I'm using this approach so I can add a new parameter in Model Constraint, since I understand that command cannot include variables specified only as auxiliary variables. It seems like this approach is the one described by Masyn (http://www.depts.ttu.edu/immap/Masyn_LCAWorkshop_Dec2013_TTU.pdf, slide 119). My main consideration is to be able to create the linear combination parameter.
Is fixing the thresholds this way what Mplus is doing with Auxiliary (R) or (R3STEP)?
Great! So back to the initial question--I'm not sure what could be going on that would make the coefficients estimable when all coefficients are included, but force them to be fixed when the first set and again the second set are added. Shouldn't the additional covariates make it harder to estimate parameters and not easier?