Strictly positive chi-square dif test... PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
Message/Author
 Mike Stoolmiller posted on Wednesday, June 01, 2011 - 2:32 pm
I'm trying to implement the Satorra-Bentler strictly positive chi-square difference test as described in Mplus Web note 12 for a factor mixture model with 4 classes. I have followed the procedure outlined in the web note except that to prevent Mplus from updating the start values, I have set convergence, mconvergence, logcriteria and rlogcriteria parameters to large values. Despite all this, Mplus takes a 2nd iteration in the EM algorithm regardless of how high I set the convergence parameters. If I put in start values with more then 3 significant digits, like 6 significant digits, which is considerably more work then using the svalues feature, Mplus still takes a 2nd iteration even though almost nothing changes. Is there something else that has to be set?
 Tihomir Asparouhov posted on Wednesday, June 01, 2011 - 3:08 pm
Mike

Try miter=1 or send your example to support@statmodel.com

Tihomir
 Mike Stoolmiller posted on Wednesday, June 01, 2011 - 11:44 pm
I tried setting miter=1 and then Mplus tells me that an insufficient number of E steps have been taken and I don't get the MLR scaling factor that I need to compute the strictly positive test. I will send you the example and data.
 Alithe van den Akker posted on Wednesday, January 25, 2012 - 1:08 pm
I am running into the same problem as presented above with estimating an m10 model. I have tried increasing the convergence, but there are iterations in the 'gradient' and 'quasi-newton' sections. Is there any more news on this issue? Thank you.
 Linda K. Muthen posted on Wednesday, January 25, 2012 - 1:42 pm
Please send the relevant files and your license number to support@statmodel.com.
 Naomi Friedman posted on Wednesday, August 08, 2012 - 10:28 pm
I am also running into this same problem with type=random and algorithm=integration. When I set miter=1, it tells me "THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN INSUFFICIENT NUMBER OF E STEPS. INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED" and it does not give me any loglikelihood or scaling factors.

Was there a resolution to this problem that would allow me to get the scaling factor for the M10 model?
 Linda K. Muthen posted on Thursday, August 09, 2012 - 4:37 pm
Please send the output and your license number to support@statmodel.com.
 Nicholas Bishop posted on Wednesday, November 14, 2012 - 9:20 pm
Can anyone report back on the solution to this issue?

Here is the analysis statement I am using to produce the m10 model:

ANALYSIS:
type=mixture random;
ESTIMATOR=mlr;
PROCESSORS = 8;
algorithm=integration;
!convergence=100000000;
miter=1;

Thanks!
 Linda K. Muthen posted on Thursday, November 15, 2012 - 6:03 pm
You should not comment out the CONVERGENCE option.
 Nicholas Bishop posted on Thursday, November 15, 2012 - 8:01 pm
Hi Linda,
When I include the MITER statement, I received the following warning whether or not I have the convergence=100000000 statement included:

THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN INSUFFICIENT NUMBER OF E STEPS. INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED.
 Linda K. Muthen posted on Friday, November 16, 2012 - 6:01 pm
Please send the output and your license number to support@statmodel.com.
 Sarah Herpertz posted on Friday, June 26, 2015 - 7:54 pm
Dear Muthéns,
I performed a LMS model with 2 groups (type=mixture random; algorithm=integration).
Subsequently, I computed a SB scaled Chi square difference test to compare a model without the interaction term (Model 0) to a model with the interaction term (Model 1). Thus the result of the SB scaled Chi square difference test was negative, I tried to estimate a third model (Model M10).
I followed web note 12, example 1.
My models are:
M0 – Model without interaction term, output: svalues;
M1 – Model with interaction term in each group;
M10 – 1) Model produced by svalues; 2) adding the interaction term in each group (f3 on f1xf2)
Question: Is this the right procedure for LMS models? I am asking because the scaling correction factor of M10 is smaller than the scaling correction factor of the M1 model – thus the chi-square is still negative. Mplus is fixing the interaction term automatically to zero.
Thank you very much.
 Bengt O. Muthen posted on Saturday, June 27, 2015 - 10:38 pm
If you have only a single XWITH interaction you only need the z-test for its slope.
 Sarah Herpertz posted on Monday, June 29, 2015 - 2:54 pm
Thanks!
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: