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 Jan Newman posted on Friday, March 01, 2013 - 4:47 pm
For my dissertation, I am trying to run a logistic regression model - about 19 predictors on a single binary dependent variable. My missing data is not missing randomly and I cannot do multiple imputation. It was suggested that I use mplus to avoid listwise deletion.

I got an error message and need help running this analysis or knowing if it is possible.

I got the following warning:

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

Here is the syntax I ran to get this error message based on recommendations from UCLA's site.

Title:
Diss LR Model - General Recidivism

Data:
FILE IS "C:\Users\jln111\Desktop\recjn.dat";

Variable:
NAMES ARE x1 x2 x3 d1;
USEVARIABLES ARE x1 x2 x3 d1;
categorical = d1;
MISSING ARE ALL (-99);

Analysis:
estimator = ml;
integration = montecarlo;

Model:
d1 on x1 x2 x3;
[x1 x2 x3];

Output:
sampstat standardized;
residual;

Our department's copy of mplus was purchased over a year ago, so I am not sure we can get customer support now. Please help!!
 Linda K. Muthen posted on Saturday, March 02, 2013 - 12:01 am
Try removing INTEGRATION = MONTECARLO; or use INTEGRATION = MONTECARLO (5000);
 Jan Newman posted on Saturday, March 02, 2013 - 4:11 am
Thank you so much, Dr. Muthen for your prompt reply. I will try this next. I read something about using MLR instead of ML as the estimation method. Would that address this issue or is it unrelated?
 Linda K. Muthen posted on Saturday, March 02, 2013 - 12:46 pm
I don't think this will change anything but you can try.
 Jan Newman posted on Saturday, March 02, 2013 - 7:44 pm
Thank you, Dr. Muthen. I hope this is a final question. I rechecked my assumptions, and everything works for the other logistic regression assumptions, but there is non-normality that may be affecting my analysis. I have a large (n=1000) longitudinal dataset of a clinical sample, and I am looking at 19 predictors. These factors are accounting for much of the non-normality. Then the MNAR issue is due to different researchers phasing measures in and out and cases are missing for big segments of people. Most predictors are missing less than 5%, but 2 are missing around 18-23%. Does the MNAR and non-normality suggest a different estimation method or other step? I apologize for all of the questions - I am very new to mplus.
 Jan Newman posted on Saturday, March 02, 2013 - 8:00 pm
Also, all predictors are continuous... (I apologize for taking up 2 comment windows).
 Bengt O. Muthen posted on Sunday, March 03, 2013 - 5:29 pm
I don't think non-normality in the predictors with missing data on the predictors affect the results very much. Missingness that is at most 23% does not seem extreme. You say MNAR, but you may have MAR reasonably well approximated. Note that MAR allows selective missingness (despite its name).
 Jan Newman posted on Sunday, March 10, 2013 - 2:15 am
I was able to run my logistic regression model with MLR and monte carlo integration successfully after rescaling variables due to large variances.

Now, I would like to run the model and examining the two groups. Can I do this with mplus in logistic regression? I have two different groups of offenders. All of my predictors are continuous - only the grouping variable is categorical. When I tried to do it using GROUPING command it said that I needed a knownclass command, but since this isn't a latent structure, I wasn't sure if it would work? Is this in the user's guide and I am missing it?
 Linda K. Muthen posted on Sunday, March 10, 2013 - 8:47 pm
MIXTURE KNOWNCLASS is the same as GROUPING. When the classes are based on an observed variable, there is not mixture modeling. See the CLASSES and KNOWNCLASS options.
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