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Hello 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 


You would need to use manual 3step for this. See Web Note 21, Section 3.2. 


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 would use the recommendations in Web Note 21. 


Hello again, 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 


See the FAQ onour website: Odds ratio interpretation for categorical distal outcomes using DCAT 


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 3step approach described in our papers. 

lisa Car posted on Friday, November 18, 2016  10:19 am



Hi again, I have tried using the manual 3step 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 WITHINCLASS VARIATION FOR THE VARIABLE. I have tried increasing STARTS up to 800 100 with no avail. thank you 


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? 


No. 

DavidBoyda posted on Monday, April 16, 2018  9:41 pm



Dear support, If I have categorical variables, and temporal order is not an issue, can i use DCAT instead of r3step to predict class membership instead of using it for distals? (e.g N > dummy coded age categories) 


I don't see how that would work. 

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