Latent Profile Analysis PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
Message/Author
 Levent Dumenci posted on Friday, May 09, 2003 - 5:20 am
Here is my syntax:

type is mixture;
model:
%overall%
x1-x9;
%c#1%
[x1-x9*-1];
%c#2%
[x1-x9*0];
%c#3%
[x1-x9*1];

True or False: Observed covariance matrix is class specific.
Thanks,
 bmuthen posted on Friday, May 09, 2003 - 6:44 am
This model implies a class-invariant covariance matrix for the x's. The covariances are fixed to zero as the default.
 Levent Dumenci posted on Monday, May 12, 2003 - 6:20 am
That's correct. I estimated class-invariant covariance matrix (diagonal), as I intended. However, the residual covariance matrices (Observed - estimated) vary across classes, which led me think that observed covariance matrices differ across classes. Is that right?
 bmuthen posted on Monday, May 12, 2003 - 7:28 am
Here "Observed" is a class-specific covariance matrix computed by weighting the raw data with the individuals' posterior probabilities as estimated from the model. This means that the "observed" covariance matrix does change over the classes. This is the closest to observed that one can get with unknown mixture classes.
 Anonymous posted on Friday, May 14, 2004 - 11:28 am
I am new to Mplus and I need help on my problem below.
I have a structural model has two exogenous latent models effecting one endogenous latent variable. One of the exogenous latent variables has four categorical manifest variables and the other exogenous latent variable has three manifest variables. The endogenous latent variable is continuous and has 4 continuous manifest variables.
I am considering using latent profile analysis and the Mplus program. Am I right and how do I go about it.
Thanks so much for your assistance.
 Anonymous posted on Friday, May 14, 2004 - 12:35 pm
Sorry I did not preview my first message. Below is the corrected version.
I am new to latent variable modeling (not SEM) and Mplus. I need help to solve a modeling problem.
I have a model that has two exogenous latent variables effecting one endogenous latent variable. The latter has four continous manifest variables. One of the exogenous latent variables has four categorical manifest variables and the other has three continous manifest variables. I want to use latent profile analysis and Mplus. Am I right in using LPA? Can Mplus perform the analysis?
Thanks
 Linda K. Muthen posted on Friday, May 14, 2004 - 3:44 pm
So you want to look for unobserved heterogeneity in your data in the form of latent classes using an SEM model?
 Anonymous posted on Tuesday, May 18, 2004 - 7:19 am
Yes.
 Linda K. Muthen posted on Tuesday, May 18, 2004 - 7:44 am
There are two papers on the homepage at www.statmodel.com that describe such models using Mplus. The first author is Lubke.
 Scott Weaver posted on Wednesday, August 02, 2006 - 11:30 pm
I am conducting a latent profile analysis. I have specified tech11 to get the LMR test, but am having difficulty figuring out how to specify the start values such that the first class is the smallest class. I tried specifying the start value for the latent class mean (C#1*-2 in a 2 class model) but that does not seem to work to make class 1 be the smallest class.

Your help is very much appreciated!
 Linda K. Muthen posted on Thursday, August 03, 2006 - 6:57 am
You should be specifying starting values so that the largest class is last. You do this by using the parameter values of the means of the latent class indicators not the means of the categorical latent variable.
 Scott Weaver posted on Thursday, August 03, 2006 - 12:05 pm
I tried what you suggested, but it does not seem to be working either. In my initial run of a 2 class model, the largest class is first. So I used the estimated means from the largest class as start values for the last class (%c#2%) and ran the model with starts = 0 0. I verified that my specified start values were used with tech1. However, the results still are such where the largest class is first. Any suggestions?
Thanks!
 Linda K. Muthen posted on Thursday, August 03, 2006 - 12:43 pm
You would need to send your input, data, output, and license number to support@statmodel.com so I can see where you are going wrong.
 Zhongmiao Wang posted on Thursday, November 02, 2006 - 9:32 am
any opinions about the difference between Latent Profile Analysis and Latent Class Analysis? From the Lubke and Muthen's paper about factor mixture models, I think for LPA, the latent class indicators are continuous variables, but for LCA, the latent class indicators are categorical or ordinal variables? Am I right?
 Linda K. Muthen posted on Thursday, November 02, 2006 - 1:13 pm
That sounds correct.
 Raji Srinivasan posted on Saturday, February 03, 2007 - 7:46 pm
This is a question regarding the output file from a mixture model.

I am saving the output from a mixture model with the cprobs for segment membership- but I would also like to get the id's of the observations - in order to do some additional post hoc analysis using other variables not used in the mplus models.
Is there a way that I can do this?
In other words, can I get mplus to carry forward an id variable from the input data file into the output data file

Thanks in advance!
 Linda K. Muthen posted on Monday, February 05, 2007 - 6:45 am
If you include the IDVARIABLE option in the VARIABLE command, the id variable will be saved.
 Michelle Finney posted on Friday, June 06, 2008 - 12:12 pm
Hi
I have pre and post measures on five cognitive domains for a large sample of healthy older adults with a family history of Alzheimer's disease. The five pre-and post measures were adjusted for age, gender, and IQ using data from a control sample
We are interested in identifying 3 possible groups in terms of cognitive performance at both time points: improver, stable, or decliner.
I have thought about 3 possible approaches to analyze the data in MPLUS
1. Include all 10 measures (5pre and 5post) as indicators of class membership. Do latent Profile analysis.
2. Compute five change scores: post - pre, in which case a negative score would indicate decline, and then use both the five change scores and also the Time 1 scores as indicators of class membership. (Including performance at time 1 and also change would capture those with low initial performance and also a decline.)
3. Assign a score to each individual on each of the five cognitive dimension according to the following scheme:
Assign 2 to those performing 1SD or more above Controls
Assign 1 to those performing within +/-1SD relative to controls
Assign 0 to those performing 1SD or more below Controls.
Use these 10 categories in a latent Class Analysis.

What would be the best approach to identify decliners, stables, and "improvers"?

Thank you very much in advance for your help!

MF
 Bengt O. Muthen posted on Friday, June 06, 2008 - 12:26 pm
How about Latent Transition Analysis, where you have a latent class model for the 5 outcomes at each of the two time points? With say 2 classes at each time point you would have a chance to get the decliners, stables, and improvers.
 Michelle Finney posted on Friday, June 06, 2008 - 1:24 pm
Dr. Muthen,

Thanks for your response.

Could you point me to an example of the commands set up using MPLUS?

Thank you!
MF
 Linda K. Muthen posted on Friday, June 06, 2008 - 1:44 pm
See Example 8.13 and 8.14 in the Mplus User's Guide. See also the Nylund dissertation that is on the website.
 Thomas Olino posted on Friday, August 07, 2009 - 5:25 pm
Are there any guidelines for the minimum number of continuous indicators for latent profile analysis? Alternatively asked, is there a way to calculate df for latent profile analysis?

Thanks!
 Linda K. Muthen posted on Friday, August 07, 2009 - 6:09 pm
Degrees of freedom are not relevant for LPA because there is no unrestricted set of sample statistics to test against. I know of no guidelines for the minimum number of continuous indicators for latent profile analysis.
 Tomoko Udo Schaller posted on Wednesday, September 02, 2009 - 9:49 am
Dr. Muthen,

I'm using Latent Profile Analysis on the data collected from three different sample sources. I could try to use sample sources are a covariate, but am wondering if there are any other ways to take different sample sources into account in the analysis.

Thank you.
 Bengt O. Muthen posted on Wednesday, September 02, 2009 - 11:30 am
You can treat sample source as "KNOWNCLASS" which makes it possible to test equality across samples of any of the parameters in your model.
 Melissa Kimber posted on Wednesday, February 22, 2012 - 8:22 am
Hello Dr.'s Muthen,
I have run an LPA on three continuous indcators and have found a good 4 class model. I would now like to use that Latent class variable as 'predictor,' if you will, in another LV model that has dependent variable that is defined by 3 binary indicators and ther observed covariates.
Is there any syntax or examples about how to do this?
Thank you,
***Melissa
 Bengt O. Muthen posted on Wednesday, February 22, 2012 - 10:17 am
Look at how UG ex8.6 handles the influence of c on u. You don't say u ON c, but the u thresholds/means vary over the c classes by default.
 Erika Wolf posted on Wednesday, April 11, 2012 - 1:34 pm
How do I request output with the observed-expected residual covariance matrix for a latent profile analysis when type = complex mixture?
 Linda K. Muthen posted on Wednesday, April 11, 2012 - 3:49 pm
Use the RESIDUAL option of the OUTPUT command.
 Kathryn Modecki posted on Saturday, August 17, 2013 - 3:07 am
Dear Dr.'s Muthen-I am running a LPA with decision making variables. However, theoretically I should include indicator variables that "overlap" -for instance, benefits of taking a risk, costs of taking a risk, and "depth of processing" (the sum of all benefits and costs), and benefit-to reward-ratio (benefits/costs). This would be an issue in regression, I believe, according to Cohen, Cohen, West, & Aiken. From your view, is there a similar issue in LPA? The solution converges in Mplus but my concern is that variables with "overlap" may be less likely to "drive" the differences across profiles. Thank you very much for your time.
 Bengt O. Muthen posted on Sunday, August 18, 2013 - 1:50 pm
In regression you worry about collinearity (too high correlation) among the predictors (covariates; x's; IVs), but in LPA your variables are outcomes (y's; DVs) so that issue isn't involved. Still, overlapping indicators may create residual covariances in LPA which may cause BIC to point to too many latent classes. If this is a concern, you can also do mixture modeling with all covariances in the model in line with UG ex 7.22.
Back to top
Add Your Message Here
Post:
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Password:
Options: Enable HTML code in message
Automatically activate URLs in message
Action: