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 Alicia Merline posted on Monday, March 07, 2005 - 3:48 pm
Hi, my name is Alicia Merline. I attended the November Mplus training in VA. I am using Mplus to perform GGMM on substance use data across 5 timepoints.

Mplus produced the following error message:

THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILL-CONDITIONED
FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES.

THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-POSITIVE
DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES
BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION
NUMBER IS -0.549D-16.

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE
COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE
AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR
STARTING VALUES. PROBLEM INVOLVING PARAMETER 14.

I have been getting similar error messages consistently regardless of which data I use (cigarettes, alcohol or marijuana) and the number of classes I specify. Sometimes the parameter number is different. I have tried fixing the problemed parameter to zero and this results in output stating that next parameter has a problem.

Here is an example of my input (in this case for cigarettes with 5 latent classes.

TITLE: emerging adulthood analyses
DATA: FILE IS s:\MTF\chap4rev.txt;
VARIABLE:
NAMES ARE V101 V102 V103 V104 V105 V106
V35225 V35226 V35227 V35228
V35229 V35230 V35231 V35232 V35233 V35234 V35235 V35236 V35237
V35238 V35239 V35240 V35241 V35242 V35243 V35244 V35245 V35246
V35247 V35248 V35256 V35403 V35404 V35405 V35406 V35407 V35408
V35409 V35410 V35501 V35502 V35503 V35504 V35505 V35506 V35511
V35512 V35513 V35523 V35524 V35525 V35526 V35527 V35528 V35529
V35530 V35531 V35532 V35533 V35534 V35538 V35539 V35540 V35541
V35542 V35543 V35544 V35545 V35586 V35587 V35588 V35589 V35590
V35591 V35592 V35593 V35594 V35595 V35596 V35597 V35598 V35599
V35600 V35601 V35602 V35603 V35604 V35605 V35606 V35607 V35608
V35609 V35610 V35611 V35612 V35613 V35614 V35615 V35616 V35617
V35618 V35619 V35620 V35621 V35622 V35623 V35624 V35625 V35626
V35627 V35628 V35629 V35630 V35631 V35632 V35633 V35634 V35635
V35644 V35645 V35646 V35647 V35648 V35649 V35650 V35651 V35652
V35653 V35654 V35655 V35656 V35657 V35658 V35659 V35660 V35661
V35662 V35663 V35664 V35665 V35666 V35667 V35668 V35669 V35670
V35671 V35672 V35673 V35674 V35675 V35676 V35677 V35678 V35679
V35680 V35681 V35682 V35683 V35684 V35685 V35686 V35687 V35688
V35689 V35690 V35691 V35692 V35693 V35694 V35695 V35696 V35697
V35698 V35699 V35700 V35701 V35702 V35703 V35704 V35705 V35706
V35707 V35708 V35709 V35710 V35711 V35712 V35713 V35714 V35715
V35716 V35717 V35718 V35719 V35720 V35721 V35722 V35723 V35724
V35725 V35726 V35727 V35728 V35729 V35730 V35731 V35732 V35733
V35734 V35735 V35736 V35741 V35742 V35743 V35744 V35756 V35763
V35901 V35902 V35903 V35904 V35905 V35906 V35907 V35908 V35909
V35910 V35911 V35912 V35913 V35001 V35257 V35258 V35259
a500 m500 i500 c500 yrsmarr v35266 female male v35marid
unmarid v35parnt noparnt chaway chlive white black other scollege
college ncollege cohort cohort1 v35hincm yrborn mardate v8748 drink1
drink2 drink3 drink4 drink5 ndrink1 ndrink2 ndrink3 ndrink4 ndrink5
collegem coke1 coke2 coke3 coke4 coke5 cig1 cig2 cig3 cig4 cig5
marr1 marr2 marr3 marr4 marr5 child1 child2 child3 child4 child5
cohabit1 aget1 aget2 aget3 aget4 aget5 unemp1 unemp2 unemp3
unemp4 unemp5 v8708 v8706 v8820 v8702 v8749 v8789 v8771
mj1 mj2 mj3 mj4 mj5 v8716 bcmprnt r35594 r35595 r35596 r35597 r35598
r35599 r35606 r35607 r35608 r35609 r35610 r35611 r35618 r35619
r35620 r35621 r35622 r35623 r35600 r35601 r35602 r35603 r35604
r35605 r35612 r35613 r35614 r35615 r35616 r35617 r35624 r35625
r35626 r35627 r35628 r35629 cigdat
colgradm sampl drink1d cig1d coke1d mj1d rdrink1d rcig1d spdiscaf
spdiscah rdiscah spdiscg rspdiscg spdiscc spdiscm drinkabs
cigabs mjabs time1 time2 time3 time4 time5 sample bycig bydrink
bymj byill v342 attndcol cignever mjnever drinknev mjgroup4
cgroup2 drnkgrp4 ndrkgrp4 mjgroupf cgroupf drnkgrpf
wcig1 wcig2 wcig3 wcig4 wcig5 wdrink1 wdrink2 wdrink3 wdrink4
wdrink5 wmj1 wmj2 wmj3 wmj4 wmj5 age35edu age35ses unempll
risk riskt genetica v35991
unimpmar cohabitr engaged bamarid;
CATEGORICAL = V35600;
CENSORED = cig1(b) cig2(b) cig3(b) cig4(b) cig5(b);
USEVARIABLES ARE v35600 cig1 cig2 cig3 cig4 cig5;
MISSING ARE ALL (9999);

CLASSES = c (5);

ANALYSIS: TYPE = MIXTURE;
MODEL: %OVERALL%
i s q| cig1@0 cig2@1 cig3@2 cig4@3 cig5;
%c#1%
[i*6];
[s*0];
[q*0];
%c#2%
[i*5.5];
[s*-1.5];
[q*0];
%c#3%
[i*-2.5];
[s*1.5];
[q*0];
%c#4%
[i*-6];
[s*0];
[q*0];
%c#5%
[i*-2.5];
[s*5];
[q*-2.5];
OUTPUT: SAMP TECH8 TECH12 RESIDUAL;

Although Mplus produces several warnings, the classes identified by the program look quite reasonable.

As you may be able to tell from my file name and area of research, I am part of the Monitoring the Future project at the University of Michigan. John Schulenberg is my main collaborator for the current research project. I've also discussed this project with Patrick O'Malley. Dr. O'Malley is the only other member of our group with Mplus experience.

Please suggest how I can resolve this problem.

Thank you,

Alicia
 Linda K. Muthen posted on Monday, March 07, 2005 - 4:40 pm
I am not sure if you have already done this. But I would start with two classes and only the %overall% part of the MODEL command allowing the Mplus defaults to be used. Only once I got the two class model with defaults working would I go to 3 classes and/or relax some of the defaults. Also, I would use STARTS = 100 10; instead of the default of STARTS = 10 1; if taking the stepwise approach does not work.

Also, it is not clear which version of Mplus you are using. If you cannot resolve this send your input and data along with your license number to support@statmodel.com.
 bmuthen posted on Monday, March 07, 2005 - 5:00 pm
I would second Linda's suggestions. To get insights into why your model is not identifed, you can look at tech1 to see what is parameter 14 that the error refers to. For example, if a class has only one or two individuals in it, parameters specific to this class are not identified. Also, I would recommend considering 2-part modeling as an alternative to censored modeling - it is both easier to work with (faster) and more flexible (refs on our web sie).
 Alicia Merline posted on Tuesday, March 08, 2005 - 4:35 pm
Thanks for replying so quickly.

I have done the most work with the alcohol model, so I'm just going to talk about this model right now.

The two class model works fine with no error messages. When I move to a three class model the problems begin. I tried not specifying starting values and still get errors. In this case, the parameter 14 is listed as causing the problem. 14 is the Alpha for the intercept of the third latent class (-10.365). Size of groups is not a problem. The smallest group has 481 members.

When I run this same model but using start values, Parameter 5 is listed as having a problem. 5 is the Alpha for the quadratic term in latent class 1 (159.714).

I should note that latent class 3 in the unrestricted model and latent class 1 in the model with starting values are the same.

I tried a new model with starting values for the first two classes only. In this model I again get a problem with the intercept for the same group (now classified into group 2).

When I look back at my previous attempts at getting a 5 class solution I see that the problem class has a mean of 1 at each time. This is true of the problem clss in the 3 class solution. So, the real issue seems to be with the (large) class of respondents who abstain from heavy drinking at all 5 timepoints.

Thank you for your suggestions, they certainly helped me to define the problem more clearly.

I think that I have two options now--exclude abstainers from analyses (we have a history of doing this at MTF) or perhaps try the 2-part modeling. Do you think 2-part modeling would solve the problem?

Thank you again,

Alicia
 bmuthen posted on Tuesday, March 08, 2005 - 5:34 pm
Why don't you send your input, output, and data to support@statmodel.com - we can probably see the problem with your 3-class GMM.
 Alicia Merline posted on Wednesday, March 09, 2005 - 4:30 pm
Bength,

Sending my input and output would not be a problem. However, because of its sensitive nature, accesss to the MTF data is limited quite strictly. I am inquiring with the PIs about possibly sharing data with you.

I installed the update today and now the error messages have changed completely. Now I get a message stating that two parameters have been fixed to achieve identifiability. These two parameters are the alphas for the intercept and slope of the abstainer group. I am assuming that they have been fixed at 0, is that correct?

Alicia
 Linda K. Muthen posted on Wednesday, March 09, 2005 - 6:21 pm
You can see what they are fixed at in the output. This happens when values go very large. The standard errors are fixed at zero.
 Anonymous posted on Thursday, April 07, 2005 - 4:57 pm
Hi
I am using GGMM for longitudinal data with missing values. here is the warning messages from Mplus when I am using mixture missing;

THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY. THE COMPUTATION OF
THE FISHER INFORMATION MATRIX COULD NOT BE COMPLETED.

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE
COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE
AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR
STARTING VALUES. THE SAMPLE COVARIANCE MATRIX COULD NOT BE INVERTED FOR
CLASS 2. PROBLEM INVOLVING VARIABLE X4.

X4 is a dichotomous covariate.
the output doesn't have goodness of fit test due to the warnings. I am just wondering if the estimates for the parameters are still reliable.
Thanks,
Lulu
 Linda K. Muthen posted on Friday, April 08, 2005 - 8:41 am
It is likely that x4 has no variability in class 2. In this case, the regression coefficient for x4 cannot be determined. Fit statistics are not available for mixture models. Also, be sure to use Version 3.12.
 Socrates posted on Monday, February 20, 2006 - 3:13 pm
Dear Dr. Muthén

When running different GMMs on different datasets, I often get the following error warning:

THE MODEL ESTIMATION TERMINATED NORMALLY

WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 1 IS NOT POSITIVE DEFINITE. PROBLEM INVOLVING VARIABLE s.

In this discussion, I read that this error warning is serious. However, what can I do to avoid it / to deal with it? Since, as I mentioned before, this message appears very often, I have the feeling that perhaps it is a technical problem?

Thanks and best regards!
 bmuthen posted on Monday, February 20, 2006 - 3:22 pm
A typical cause is that your estimated variance for s is negative. This suggests fixing this variance at zero so that s is a fixed rather than a random effect. This happens more often than in single-class growth models because the classes absorb much of the variation in the growth factors.
 Socrates posted on Tuesday, February 21, 2006 - 6:20 am
Dear Dr. Muthén

Many thanks for the fast reply! While the estimated variance for s is not negative, I found in the TECH4 output a correlation of the variable s with another variable s2 that is greater than one. s is the slope of a process A and s2 the slope of a parallel process B. Does this circumstance also suggest fixing the variance of s at zero?
 bmuthen posted on Tuesday, February 21, 2006 - 9:27 pm
No it does not. A common mistake with parallel processes is to omit contemporaneous correlations between the outcome residuals for the different processes. This then channels too much of the correlation between the observed outcomes through the growth factors, causing unit correlation.
 socrates posted on Thursday, November 02, 2006 - 7:01 pm
dear dr. muthen

running a GGMM with predictors of class membership probabilities and growth parameters results in the following error message:

WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 1
IS NOT POSITIVE DEFINITE.
PROBLEM INVOLVING VARIABLE S.


WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 2
IS NOT POSITIVE DEFINITE.
PROBLEM INVOLVING VARIABLE S.


WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 3
IS NOT POSITIVE DEFINITE.
PROBLEM INVOLVING VARIABLE S.


WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 4
IS NOT POSITIVE DEFINITE.
PROBLEM INVOLVING VARIABLE S.


WARNING: THE RESIDUAL COVARIANCE MATRIX (PSI) IN CLASS 5
IS NOT POSITIVE DEFINITE.
PROBLEM INVOLVING VARIABLE S.

s ist the residual variance of the slope. it is negative and does not differ significantly from zero. when fixing s@0 the model converges without error message.
is this prcedure sound? and if not: what can I do to avoid the problem of negativ residual variances?

many thanks!
 Linda K. Muthen posted on Thursday, November 02, 2006 - 7:13 pm
This is a way many people solve this problem.
 Matt Moehr posted on Tuesday, January 16, 2007 - 7:03 pm
I have a couple of follow-up questions regarding the error message for non-positive definite psi matrices, and the subsequent fixing of S.

1) What does the negative variance for S indicate? There's no variation among the individual slopes? I'm running a single class model (although with multiple cohorts), so the class differences aren't sucking up all the variation. Also, the individual plots would seem to contradict this.

2) By assuming that the variance of S is zero, does the interpretation of any other parameter change? Can I and S still be used in the linear equation, y = I + S*x, to come up with predicted values?

3) I went ahead and used S@0, but then got this warning:
"All continuous latent variable covariances involving S have been fixed to 0 because the variance of S is fixed at 0." So I fixed S@1, and I got a model with good looking estimates but much worse model fit. Can I pick arbitrary, non-zero values for the variance of S?
 Linda K. Muthen posted on Tuesday, January 16, 2007 - 7:53 pm
1. It can indicate the the slope growth factor is a fixed effect instead of a random effect or it can indicate that the model is not appropriate for the data.

2. No. Yes.

3. It makes sense to fix the variance to zero if it is is very small and non-significant. I don't think fixing the variance to one or another arbitrary value is justified.
 mihyun park posted on Wednesday, April 06, 2011 - 8:07 am
dear dr. Muthen


Mplus produced the following error message:

***WARNING
Data set contains cases with missing on all variables. These cases were not iincluded in the analysis. Number of cases with missing on all variables: 1
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS


After this warning, i repeat again..and then, number of observations on my result output decreased 2843 to 1422. I don't know why this result happened and what tells me..
 Linda K. Muthen posted on Wednesday, April 06, 2011 - 4:25 pm
Please send the two outputs and your license number to support@statmodel.com.
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