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 Daniel posted on Monday, August 25, 2003 - 9:16 am
I just read the paper by Gitta Lubke and Bengt about monte carlo studies of factor mixture models. I have two questions. First, how does one estimate Mahalanobis distance? Second, am I to understand that there isn't much difference in model performance (in terms of parameter coverage and average posterior probability) between a model with and without class-specific variance? Is it better to allow for class-specific variance in, for example, a growth mixture model, than to assume no variation, or does it not make much difference?
 BMuthen posted on Tuesday, August 26, 2003 - 10:00 am
I think the formula for the Mahalanobis distance is in the paper. Otherwise, see a multivariate statitics textbook. It can be done by using, for example, SAS proc matrix.

Regarding your second question, as far as the data for this paper is concerned, you are correct. It may not be correct for all data.

Regarding class-specific variances, they can make a big difference if they are needed to fit the data. For example, studying problematic behavior development over time, one often sees a normal class which shows a low mean and very little variation over time whereas other classes may have a lot of variation.
 Daniel posted on Tuesday, August 26, 2003 - 10:12 am
Thank you
 Levent Dumenci posted on Monday, December 13, 2004 - 11:58 am
I have a question on the following portion of the output file:

CATEGORICAL ARE x1-x10;
CLASSES = c(3);

ANALYSIS:
TYPE IS MIXTURE;
MODEL:
%OVERALL%
F by x1-x10;
SAVEDATA:
FILE IS outdat.dat;
save = fscores;

*** ERROR in Analysis command
OUTPUT options FSCOEFFICIENT and FSDETERMINACY and SAVEDATA SAVE option
FSCORES are not available when there are no latent variables.

F is a latent variable. What I really want to do is to estimate different factor structures across classes and save the estimated fscores plus the cprob. By the way, I encountered no problem in saving the cprob.

Thank you
 Linda K. Muthen posted on Tuesday, December 14, 2004 - 9:08 am
The default with mixture and categorical is to fix the factor variances to zero. Therefore no factor scores etc. are available. I think you need to add ALGORITHM = INTEGRATION to your ANALYSIS command.
 Daniel Bontempo posted on Wednesday, January 05, 2005 - 1:58 pm
Linda -

Is there information on how the factor scores are computed and what are the properties of the factor scores (e.g., validity, preserved factor correlations)? Does it matter if the indicators were continuous or categorical?

Thanks
 Linda K. Muthen posted on Wednesday, January 05, 2005 - 3:44 pm
See Technical Appendix 11 on the website for a description of factor scores for continuous and cateogical outcomes.
 Andrew Baillie posted on Thursday, August 03, 2006 - 6:11 pm
Linda or Bengt,

Could I check how a "zero class" as you used in

Bengt Muthen and Tihomir Asparouhov, Item response mixture modeling: Application to tobacco dependence criteria, Addictive Behaviors, Volume 31, Issue 6, June 2006, Pages 1050-1066.
(http://www.sciencedirect.com/science/article/B6VC9-4JW15NF-6/2/fa328cda89499c0510baddb575a4512b)

is tested?

I guess that i'ts done by testing for two classes and setting the thresholds for each item to be -15 for one class? Is that right and is there anything else to be done to test a zero class?

thanks

Andrew

(andrew.baillie AT mq.edu.au)
 Bengt O. Muthen posted on Thursday, August 03, 2006 - 7:23 pm
You fix the thresholds at +15 for the zero class and you also fix factor means and variances at zero for this zero class.
 Andrew Baillie posted on Tuesday, August 08, 2006 - 7:32 pm
Bengt,

thanks for your quick response (and for the excellent support!)

I'm wondering about setting the factor means for a zero class to zero. I was expecting that factor scores would be standardised so that zero would be the grand average? That way the zero class "could" have a mean that was greater than the other classes? (This sounds v. unlikely with thresholds at +15)

thanks again

Andrew

(andrew.baillie AT mq.edu.au)
 Bengt O. Muthen posted on Wednesday, August 09, 2006 - 7:57 am
For the zero class you should think of the factor as not existing - when you fix both the mean and the variance of the factor to zero it no longer influences anything in the model.

For the remaining classes, the average of the factor means across classes is not standardized to zero. Instead, a reference class with zero factor mean is used.
 Andrew Baillie posted on Sunday, October 29, 2006 - 2:28 am
Bengt & Linda,

I'm wondering about the magnitude of the differences between the models in say yor paper with Tihomir Asparouhov. Given some of the analysis compares -2logliklihoods between nested models what do you think about using the omega^2 effect size as an index of the magnitude of the differences between models? Are there any other alternatives?

thanks

Andrew

andrew.baillie AT mq.edu.au
 Bengt O. Muthen posted on Sunday, October 29, 2006 - 3:26 pm
For nested models I like to use LR chi-square based on 2*LL diffs - when that is correct (no parameters on the border). I don't know about omega. We are also contemplating generalizing the bootstrapped LR test to models that differ not only in number of classes but also in terms of number of random effects.
 anonymous posted on Monday, April 02, 2007 - 3:15 pm
Greetings,

I just read the forthcoming paper (Muthén, 2006) on Latent variable hybrids: Overview of old and new models.

I was wondering if inputs examples for the four cross sectional models graphically exposed in figure 1 were available (mixture factor analysis, non parametric FA, factor mixture analysis, non parametric FMA).

Thank you very much
 Linda K. Muthen posted on Monday, April 02, 2007 - 6:25 pm
Send your email address to support@statmodel.com.
 Alex posted on Friday, April 27, 2007 - 6:53 am
I am working on a latent profile analysis in which I use factor mixture models to test for conditional dependance. Following your previous suggestions, I use factor mixture models with class specific factor variance. They work generally well.
However, I also try to fit models with class specific factor loadings (i.e. 7.27). You warned me that such models could be hard to fit (and they are). For example, amongst the numerous warnings which I obtain, I often receive message regarding negative (and significant) residual factor variance within one of the class.
My question is whether factor mixture models with within class factor loadings could be easier to fit by fixing the overall factor variance to 1 instead of fixing the loading of the first indicator to 1, while withdrawing (or not ?) class specific estimates of factor variance ? Would it still be logical to compare the fit of factor mixture models with class specific factor variance (unstandardized) to standardized models with class specific loadings ?

Thank you very much in advance.
 Bengt O. Muthen posted on Friday, April 27, 2007 - 7:32 am
Yes, when the factor loading matrix is made class-specific, it is my experience that it can help to set the metric in the factor variance (@1) for each class instead of the first loading. This way, the search for the best solution is less dependent on the quality of that one item.
 Alex posted on Friday, April 27, 2007 - 8:46 am
Thank you very much for your answer.
Just to make sure I understand correctly, to "set the metric in the factor variance for each class", I only have to set the variance (f@1) in the %overal% section of the model and not in each class section.
 Bengt O. Muthen posted on Friday, April 27, 2007 - 8:54 am
That's right. Saying that in the overall makes it hold for each class (see Tech1).
 Levent Dumenci posted on Thursday, September 20, 2007 - 12:29 pm
* 10 binary observed variables of u1-u10,
* factor mixture model with 2 classes,
* one factor for each class,
* classes differ in factor means only.
what should be the statements for item thresholds and factor means under %overall%, %c#1%, and %c#2%?
 Linda K. Muthen posted on Thursday, September 20, 2007 - 12:52 pm
Factor means and thresholds are free across classes as the default so I don't think you need to say anything about them.
 Anthony Ahmed posted on Sunday, May 11, 2008 - 1:23 pm
I am currently working on a dissertation project comparing the performance of Meehl's taxometric methods to latent variable methods. I have recently purchased the Mplus program to run mixture modeling methods. Every data condition I have simulated uses four continuous variables. I plan to run Latent Profile Analysis, Latent Class Factor Analysis and Factor Mixture Analysis. Is it possible to run Latent Class Factor Analysis with continuous indicator variables and is there an example in the user's guide?

My Monte Carlo datasets are tab delimited .dat files that have variable names on the first line. I got an error message when I tried to run latent profile analysis "ERROR
Invalid symbol in data file:
"V1" at record #: 1, field #: 1." The analysis runs when I delete the variable names on the datafile. Is there a way to work around this in the format statement? I know it is possible to skip columns with fixed data formats but can I skip the first row with a free data format?
Finally, I'd like the methods to determine the number of classes that best fit the data. Would I have to analyze each dataset twice, starting with c = 1 and then c = 2 or is it possible to ask the program to do both in one analysis?
 Linda K. Muthen posted on Monday, May 12, 2008 - 10:20 am
See the following paper which is available on the website for an example of Latent Class Factor Analysis:

Muthén, B. (2006). Should substance use disorders be considered as categorical or dimensional? Addiction, 101 (Suppl. 1), 6-16.

You should just delete the line with the variable names.

You need to run the analysis separately for two, three, etc. classes.
 Orla McBride posted on Monday, September 21, 2009 - 5:33 pm
I have conducted CFA and LCA on a set of binary indicators. The results suggest evidence for a one-factor model or a three-factor solution with parallel profiles. I want to follow-up this analysis by estimating a hybrid model. I have read the papers by Muthen (2006) and Muthen & Asparouhov (2006) but I am a bit unclear about the differences between LCFA and IRT mixture modeling. I’d be grateful if you could answer the following questions:

(1) Should LCFA be estimated in favor of IRT mixture modeling when the factor is considered to have a non-normal distribution?
(2) In LCFA, is it correct that the latent classes share the same dimension and therefore the factor loadings (but not thresholds) should be equal across classes?
(3) Does it make conceptual sense to estimate both types of models on the same set of indicators? If so, in what circumstances?
 Shaunna Clark posted on Tuesday, September 22, 2009 - 11:11 am
One paper that may answer many of your questions is the forthcoming Clark and Muthen article entitled "Models and strategies for factor mixture analysis:
Two examples concerning the structure underlying psychological disorders."

But since it is currently not available, here are some responses to your questions:

First, IRT mixture models and LCFA are not separate models, but LCFA is a special case where the factor loadings and item thresholds are invariant across classes and the factor variance\covariance is zero. The only difference between the classes are the location of the classes on the factor, as indicated by the factor mean being different in each class. So, to answer question 2, the classes do share the same dimension, but the factor loadings and item thresholds are equal across classes.
 Shaunna Clark posted on Tuesday, September 22, 2009 - 11:12 am
I would argue that both the LCFA and more flexible models which relax the equality of the item thresholds and factors loadings should be applied to data, but that it should be kept in mind what each of these models implies about the underlying structure of the data. LCFA and an alternative which allows the factor variance to be estimated (this variance can be restricted to be equal across classes or non-invariant), both have the same factor running through all classes and the difference between classes arises due to having class varying factor means and potentially factor variances. Other models which relax the equality of factor loadings and item thresholds may still have the same factor in both classes depending on the difference in the estimated item thresholds and factor loadings when they are allowed to vary across classes.

Also, both the LCFA and other models which relax the equality of item thresholds and factor loadings across classes allow for a non-normal factor.
 Matt Thullen posted on Tuesday, November 10, 2009 - 10:14 am
Hello

In LCFA, How is the zero factor score interpreted? Im thinking of how to represent the factor scores within and between each class in for a model with 3 factors...like in a graph or plot.

Also with LCFA, I based my syntax off the examples in Clark & Muthen(recently posted) and I get warnings about having more equality labels than parameters. I have something resembling this for each of my classes: [u1-u12] (1-12). The model seems to run fine but Im not sure if or what I should do to address those warnings.

thank you
 Linda K. Muthen posted on Tuesday, November 10, 2009 - 1:09 pm
The zero factor score is a reference point. It is not identified as a free parameter.

I would need to see your full output and license number to understand why you get an error for the syntax you show.
 Mike Stoolmiller posted on Thursday, June 02, 2011 - 7:42 am
I'm fitting factor mixture models and I have model with 4 latent classes and a single latent factor in each class. When I request class probabilities and factor scores, I get two different factor scores, one which is labeled the same way I labeled my latent factor and one with C_ as a prefix. I searched the Mplus version 6 manual but can't find any information on this second factor score. Can you direct me to some relevant documentation?
 Linda K. Muthen posted on Thursday, June 02, 2011 - 9:51 am
I don't know of any documentation. One is mixed over classes and the other is for the most likely class.
 Mike Stoolmiller posted on Thursday, June 02, 2011 - 11:49 am
Which is which?
 Linda K. Muthen posted on Thursday, June 02, 2011 - 3:01 pm
c_ is most likely class membership.
 kelly kenzik posted on Thursday, January 31, 2013 - 6:41 am
Hello,
I have a factor mixture model with two factors and 4 classes. I am allowing the means, thresholds, and factor loadings to vary across classes. My factor loadings however have only 0.00 for SE, and 999 for for my p-value. Is this because I did not make any specifications for the factor variance and it is being held at 0?
Can I still compare these factor loadings across classes?
My code looks like this:
%overall%
y1 by u1-21;
y2 by u22-u37;
%C#1%
y1 by u1-21;
y2 by u22-u37;
[u1$1-u21$1] ;
[u1$2-u21$2] ;
[u22$1-u37$1];
[u22$2-u37$2] ;
[u22$3-u37$3] ;
[y1-y2];
...and so on for the other classes.

Thanks so much!!
 Linda K. Muthen posted on Thursday, January 31, 2013 - 7:35 am
Please send the output and your license number to support@statmodel.com so I can see what the problem is.
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