Message/Author 

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 24 members. I want to perform a latent profile analysis with 5 continuous measures. How do I model the clustering between families? 


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: *** ERROR The following MODEL statements are ignored: * Statements in the OVERALL class: C#1 ON X *** ERROR One or more MODEL statements were ignored. These statements may be incorrect or are only supported by ALGORITHM=INTEGRATION. 


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 nonindependence of the schoollevel covariates. Is using CLUSTER a good way of doing this? My current model is like this: USEVAR = IDSCHOOL TOTWGT {all individuallevel and schoollevel covariates} {variables to be clustered on}; Categorical are {Likertscale attitude items to be clustered on}; AUXILIARY = (R) {all covariates}; CLUSTER is IDSCHOOL; WEIGHT IS TOTWGT; Classes = patrn (7); Missing are all (9999); ANALYSIS: Type= mixture complex; Starts = 100 20; Second question: I want to see how covariates relate to class membershipif 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. 


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. 


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, thoughthe example in Topic 5 uses a MODEL: specification and the variables are not AUXILIARY. My output looks like this: TESTS OF CATEGORICAL LATENT VARIABLE MULTINOMIAL LOGISTIC REGRESSIONS USING POSTERIOR PROBABILITYBASED MULTIPLE IMPUTATIONS (PSEUDOCLASS DRAWS) PATRN#1 ON  Estimate  SE  Est/SE  2tailed pvalue [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. 


I think I need to see your output; please send to Support@statmodel.com along with your license number. 

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