# Confirmatory latent variable cluster analysis with mplus

I would like to do a confirmatory latent class cluster analyses (finite mixtures) with a continuous and several categorical variables. I know how I can constrain binary variables (such as Cluster A has got more males than B) using Mplus (Thanks to "Conducting Confirmatory Latent Class Analysis Using Mplus" by W. Holmes Finch) but I do not know how to do this with a continuous outcome variable (e.g. Age in cluster A is < B < C = D). I checked the Mplus website and their FAQ/discussion sites but could not find anything.

• The Mplus manual and website and forums are great. Did you look there? – Behacad Jan 31 '15 at 17:52
• Can you post the code for your model without constraints? – D L Dahly Jan 31 '15 at 17:57
• Yes, I checked the Mplus website and the FAQ/discussion sites as well as the manual (which in my opinion is rather useless if you have a very specific problem). – Stats_Monkey Feb 1 '15 at 15:43
• My model is currently very simple: Data: File is 'c:\test.csv'; Variable: Names are a b c d e age MISSING ARE all (-999) ; USEVARIABLES ARE a b c d e age ; NOMINAL = a b c d e ; CLASSES = c(5); Analysis: Type=mixture; STARTS = 500 10 MODEL: – Stats_Monkey Feb 1 '15 at 15:46
• See above my mplus code. I read "Conducting Confirmatory Latent Class Analysis Using Mplus" by W. Holmes Finch who describes to constrain inequalities to nominal data but I need to put an inequality constraint on my continous age variable, such as cluster a < cluster B = Cluster C <cluster D = cluster E. Hope this explains my problem better. – Stats_Monkey Feb 1 '15 at 15:49

[tested]

You could include the mean in each cluster:

%c#1%
[age*] (age_c1) ;

%c#2%
[age*] (age_c2) ;


And than include a constraint:

Model Constraint:
age_c1 < age_c2 ;