Missing data using logistic regression PreviousNext
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 Gail Smith posted on Tuesday, July 05, 2011 - 7:31 am
I am using logistic regression and have missing data. I added the covariates.
Below is my code.

VARIABLE:
NAMES ARE v1 - v48;
USEVARIABLES ARE v32 v5 v6 v11 v12 v13 v14 v26 v27 v30 v33 v42-v48;
MISSING ARE ALL (-99);
CATEGORICAL ARE v32 ;

ANALYSIS:
! TYPE IS MONTECARLO;
ESTIMATOR IS ml;
integration = montecarlo;
ITERATIONS = 1000;
CONVERGENCE = 0.00005;

model:
v32 on v5 v6 v11 v12 v13 v14 v26 v27 v30 v33 v42-v48 ;
v5 v6 v11 v12 v13 v14 v26 v27 v30 v33 v42-v48 ;

I get the folowing warning:
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 0.463D-18. PROBLEM INVOLVING PARAMETER 184.

If I just list the variables that have missing values, it runs with no warnings.

If I list the variables that have missing values with an *, Iit runs with no warnings but the standard errors are differnt from the run without the *.

Thank you for your help,
Gail
 Linda K. Muthen posted on Tuesday, July 05, 2011 - 7:48 am
Please send the two outputs and your license number to support@statmodel.com.
 Katie Witkiewitz posted on Thursday, September 24, 2015 - 4:35 pm
Hi Bengt and Linda,

I am running a simulation study of various missing data approaches for logistic regression analyses with a binary outcome and binary predictor. When I bring the binary predictor in the model by adding the variance term I can recover the full sample size, but the results from the ML estimation are nearly identical in some scenarios and entirely identical in most scenarios to the analyses that used listwise deletion.

I've included several auxiliary variables in the ML estimation model so I am not sure why ML is giving me the same results as listwise deletion.

Any ideas?

Thanks so much!
 Linda K. Muthen posted on Thursday, September 24, 2015 - 5:18 pm
The results will be affected only by the new cases that do not have missing on y. Only those will contribute to the estimation of the slope.
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