I am about to start an LPA analysis and have a categorical distal outcome that I would like to relate the classes to as well as other covariates that I would like use to predict class membership. I have read the article from 2014 on auxiliary variables in 3 step approaches, but it is not clear to me if it is possible to do this all together, particularly including the other covariates as predictors of class membership. Can you please clarify?
Thank you. I am familiar with BCH and had planned on using it, however I was confused by table 6 of webnote 21 that recommends BCH for continuous and DCAT for categorical. Are you suggesting that I try BCH even though my distal outcome is categorical?
I have run the LPA (with 3 classes) as mentioned above to a categorical distal outcome and in the output for one of the classes I am getting an OR of 1 with a 95 % CI of (1.0, 1.0). The other 2 classes are fine. Do you know why this may be the case?
Thank you that was very helpful. In this particular case, my last class is the one whose OR I am most interested in. Is there a way to change the order of how the classes fall out? I have read about using DEFINE but I am not sure if that could work.
You use starting values to change classes around. Request SVALUES in the output from your original run and use them in a second run where you switch them for the classes so you get the last class you want.
lisa Car posted on Thursday, November 17, 2016 - 10:45 am
Seeing as the recommended model for binary outcomes cannot accommodate covariates, I am wondering what your opinion is on using logistic regression after the fact as an option to explore them?
You can use the manual 3-step approach described in our papers.
lisa Car posted on Friday, November 18, 2016 - 10:19 am
I have tried using the manual 3-step as suggested and when I get to the final step I cannot get the model to converge. My model has 3 classes, a number of covariates and 2 binary distal outcomes. The following error message appears. THE ESTIMATED COVARIANCE MATRIX FOR THE Y VARIABLES IN CLASS 1 COULD NOT BE INVERTED. PROBLEM INVOLVING VARIABLE INCIDCFK. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 3. CHANGE YOUR MODEL AND/OR STARTING VALUES. THIS MAY BE DUE TO A ZERO ESTIMATED VARIANCE, THAT IS, NO WITHIN-CLASS VARIATION FOR THE VARIABLE.
I have tried increasing STARTS up to 800 100 with no avail.
Send output to Support along with your license number.
lisa Car posted on Monday, November 21, 2016 - 1:37 pm
So the model works with a continuous distal outcome but not a categorical one however I am getting class shifting. Does something special need to be done for a categorical distal outcome in the manual 3 sep approach?