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 Roscoe posted on Tuesday, November 02, 2004 - 2:06 pm
I have a dataset with approximately 1900 observations made up of patients (probands) and family members (brother, sister, mother, father). There are approximately 600 families, each with 2-4 members. I want to perform a latent profile analysis with 5 continuous measures. How do I model the clustering between families?
 Linda K. Muthen posted on Tuesday, November 02, 2004 - 2:46 pm
Clustering for LPA can be handled in two ways in Mplus - TYPE=COMPLEX MITXURE; or TYPE = TWOLEVEL MIXTURE; These two approaches are described in the introduction to Chapter 9 in the Mplus User's Guide. This introduction is also available at the Mplus website.
 Anonymous posted on Wednesday, April 20, 2005 - 3:29 pm
I would like to run a model similar to 7.12 LCA with covariates. Except I want it to be a profile model with a categorical covariate. I have tried to run the model with the covariate X denoted as categorical and %overall%
C#1 on x; but it doesn't run. It will run if I do not denote X as categorical.
THis is the error message:
The following MODEL statements are ignored:
* Statements in the OVERALL class:
C#1 ON X
One or more MODEL statements were ignored. These statements may be
incorrect or are only supported by ALGORITHM=INTEGRATION.
 Linda K. Muthen posted on Wednesday, April 20, 2005 - 4:35 pm
Only dependent variables should be included on the CATEGORICAL list. So it is not necessary to put x on the CATEGORICAL list.
 Tess Yanisch posted on Wednesday, January 14, 2015 - 6:08 pm
Hello Doctors Muthen and Muthen,

Similar in spirit: I'm running a LCA (LPA?) on students' attitudes about different minority groups. I have students nested in schools and covariates (which I'm using as Auxiliary (R)) at both the school and student levels. However, I don't think that school will affect what class an individual is in much; I just want to control for the non-independence of the school-level covariates. Is using CLUSTER a good way of doing this?

My current model is like this:

{all individual-level and school-level covariates}
{variables to be clustered on};
Categorical are
{Likert-scale attitude items to be clustered on};

AUXILIARY = (R) {all covariates};


Classes = patrn (7);

Missing are all (9999);

Type= mixture complex;
Starts = 100 20;

Second question: I want to see how covariates relate to class membership--if gender affects how likely a person is to be in a given class, e.g. I can't find a resource explaining how to interpret that part of my output, even on this message board, and the UG does not cover output. Do you know where I could find that information?

Many thanks for your time.
 Bengt O. Muthen posted on Thursday, January 15, 2015 - 7:56 am
Q1. Looks good.

Q2. See our video and handout for Topic 5, starting with slide 120. This covers multinomial regression with a latent nominal DV. For a general discussion of multinomial regression with an observed nominal DV, see Topic 2.
 Tess Yanisch posted on Thursday, January 15, 2015 - 6:16 pm
Thank you very much for the speedy reply!

I wish I had run across the tutorials earlier; big help there. I still have a question, though--the example in Topic 5 uses a MODEL: specification and the variables are not AUXILIARY. My output looks like this:


PATRN#1 ON ---- Estimate --- SE ---- Est/SE ---- 2-tailed p-value
[list of covariates]

What is the "Estimate" here, and what does its significance mean? Is it log odds relative to odds of being in PATRN 7, the only class not listed?

Also, am I correct in assuming that the "Latent Class [X] Thresholds" list of variables gives the log odds of that response being selected in Latent Class [X]?

Apologies if these are foolish questions. This is my first major project and I unwittingly chose a method no one in my program is familiar with.
 Bengt O. Muthen posted on Thursday, January 15, 2015 - 8:29 pm
I think I need to see your output; please send to along with your license number.
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