LCA and covariates PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Anonymous posted on Tuesday, June 14, 2005 - 8:37 am
I am new to MPLUS and LCA. I am interested in performing LCA with 4 subscale scores and 2 single items scores in a sample of 445 participants. I want like to include seven categorical covariates and 4 continuous covariates. I have tested multiple solutions (a 1 class though a 6 class solution), and I have a couple of questions regarding the results.

Here is some information that you might need
1)The output contained a warning that one or more multinomial logit parameters were fixed to avoid singularity of the information matrix…but that it terminated normally. Does this mean the model did not run properly?

2)In the categorical latent variable section (relationship between the covariates and teach class) the estimates were huge (ex. 1169.266), the SEs were low (ex 1.033) and the Estimates/SE were huge for each covariate. In three of the groups all the covariates were significant

3)In the 4th groups all the SE were 0 and the Estimates/SE were 0.

I am wondering if the program corrected the problem so this is a valid (although odd) solution.

I am also wondering if there is a limit of the number of covariates.

 BMuthen posted on Tuesday, June 14, 2005 - 9:29 am
1. No, it means that for some in some classes there is no variablity for some of the covariates so the regression coefficient is not defined.

2. This sounds like the same problem as in 1.

3. This sounds like the same problem as in 1 also. Although there is no absolute limit to the number of covariates, it is problematic when they have no variances in some classes.
 Sanjoy posted on Wednesday, June 22, 2005 - 8:01 pm
Dear Professor....This is what I want to do

Y1 and Y2 are the main dependent variables (both of them are binary 0/1). Y1 and Y2 are very much correlated as well.
X’s are the covariates. Total 14 Covaraites
Our key interest is in capturing the variation across class in the regression of Y1, Y2 on X.

I want to fit LCA with covariates

In order to make a BIC comparison between different classes, I have tried to fit three different classes (class 3, class 4 and class 5). Classes 3 and 5 works well, however while trying to fit 4 class it’s not working.

MPlus output telling …. “the standard errors of the model parameter estimates may not be trustworthy for some parameters due to a non-positive definite first-order derivative product matrix. this may be due to the starting values but may also be an indication of model nonidentification. the condition number is -0.255d-11. problem involving parameter 45.”

Also there was something more …

“one or more multinomial logit parameters were fixed to avoid singularity of the information matrix. the singularity is most likely because the model is not identified, or because of empty cells in the joint distribution of the categorical latent variables and any independent variables. the following parameters were fixed:
38 39 50 36 37 33 35 34 32 52
51 30 31 28 29 47 53 49”

Following are the excerpts from the command syntax



LOGHIGH = +15;
LOGLOW = -15;
LOGCRITERION = 0.0000001;
CONVERGENCE = 0.000001;


C#1 C#2 C#3 ON
A1 A4 A5 A8
B41 B44
F5Employ F6A F6B


Mine is version 3.12 …

Q1. Could you suggest me some remedy please!

thanks and regards
 Linda K. Muthen posted on Thursday, June 23, 2005 - 3:54 am
This is a support question. Please send it to support. It is data-model dependent and cannot therefore be answered without seeing the full output and the data.
 Sanjoy posted on Thursday, June 23, 2005 - 4:30 am
Oh ok ...that's great madam, I'm going to send you right now, thanks and regards
 Anonymous posted on Thursday, June 23, 2005 - 5:42 am
Hello again,
I posted the 1st message of this thread. I am becoming more familiar with MPlus and like the program. Just to remind you I would like to use LCA to derive the best model for 4 subscale scores and 2 single items scores. I have a sample of 445 participants. I have changed the covariates (now 6 continuous covariates). I have tested multiple solutions (a 1 class though a 6 class solution).
Since all the measured variables correlate I am wondering if I relax the default of zero covariances across all models or is it solution specific? Also in example 7.22 you suggest starting values. I am wondering what strategy you would recommend for selecting these values.

 Sanjoy posted on Thursday, June 23, 2005 - 9:26 am
Dear Madam ....since morning I have tried to send you the files three times, but for some reasons it failed to reach you ...I got email notice like "Sorry, unable to deliver your message to for
the following reason:

552 Quota violation for
thanks and regards
 BMuthen posted on Friday, June 24, 2005 - 1:57 am
The correlations among the measured variables are accounted for by having several latent classes. The fact that the within class correlation is zero does not mean that the correlations among the measured variables is zero.

Usually start values are guided by theory. If you don't have a good feel for what they should be, I suggest using the default.
 Linda K. Muthen posted on Friday, June 24, 2005 - 1:58 am
Try again tomorrow.
 Sanjoy posted on Friday, June 24, 2005 - 4:08 pm
Dear Madam ... failing to send those files once again, I send them to Maija ...she said she will redirect them to you ...thanks and regards, to all of you
 Linda K. Muthen posted on Saturday, June 25, 2005 - 4:30 am
I think the files have been received several times by now. This will be looked at after July 1.
 Sanjoy posted on Saturday, June 25, 2005 - 2:33 pm
Oh ! is that so ... my gmail account was acting weird, thanks for your response!
 Raheem Paxton posted on Friday, August 17, 2007 - 12:54 pm
I was wondering if there was a difference in output if you specified covariates in the USEV statment but never regressed the covariates on the class structure (no model statement) versus regressing them on the classes (e.g., Model: c#1 - c#3 on A B C).

Thanks in advance
 Linda K. Muthen posted on Friday, August 17, 2007 - 1:52 pm
All variables on the USEVARIABLES statement are used in model estimation. If they are not used as covariates, they will be used as latent class indicators in a latent class analysis.
 Anjali Gupta posted on Monday, September 21, 2009 - 11:06 am

I'm unsure how to model a LCA with 1 class and covariates. If an example exists, please let me know.

Thank you,
 Linda K. Muthen posted on Monday, September 21, 2009 - 4:25 pm
The multinomial logistic regression of a categorical latent variable on a set of covariates requires a minimum of two classes.
 Jerry Cochran posted on Thursday, March 17, 2011 - 11:00 am

I have a question about LCA and co-variates.

I am fitting a LCA model and am following the procedure for determining classes from Nylund et al. 2007.

My question is at what point do I take entropy into account--the model with co-variates or the model without co-variates?

Or, is entropy even the best metric for quality of classification? Is using the classification table based on posterior probabilities a superior method for quality of classification?

Thank you for your time.
 Bengt O. Muthen posted on Thursday, March 17, 2011 - 4:43 pm
You want to use for entropy or classification table the final model you settle on.

Entropy is a single-number summary, so the classification table gives you more information. For instance, you may easily tell some important classes apart, but perhaps not all of them.
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