Hello, SAS experts! I am running a GEE model on the same count data. It fails to converge in Proc Genmod but converged in Glimmix. I want to understand why. Here below are my code examples: proc genmod data=ds;
class region center;
model count=region center(region) covar1 covar2/ dist=nb;
run;
proc glimmix data=ds empirical=mbn method=laplace;
class region center;
model count=region center(region) covar1 covar2/ dist=nb;
NLOPTIONS tech=nrridg gconv = 0;
run; Ideally, region and center should be treated as fixed effects. But I want to get LSM estimate out of those two factors, so I put them as fixed effects in the model. In addition, I have a sparse input matrix, i.e. , at some centers, I have zero counts. It seems to be the main cause for non-converge issue. The genmod outputs warnings such as below when failed to converge: WARNING: The relative Hessian convergence criterion of 19.752778094 is greater than the limit of 0.0001. The convergence is questionable.
WARNING: The procedure is continuing but the validity of the model fit is questionable.
WARNING: The negative of the Hessian is not positive definite. The convergence is questionable I read relevant papers e.g. Paper SAS2179-2018 from K. Kiernan. It seems that the methods of estimation and optimization would be similar in genmod and glimmix when no random affect is specified. In particular, I used "method=laplace" and "tech=nrridg" options for glimmix. According to SAS online docs: "The essential difference between the estimation approaches taken by the GLIMMIX procedure and generalized estimating equations is that the latter approach estimates the covariance parameters by the method of moments, whereas the GLIMMIX procedure uses likelihood-based techniques". My questions are as follows: 1. Why my models get converged in glimmix but not in genmod? I tested this in a couple of data set. And I compare results on the same data. 2. Does the estimation approach of covariance parameter affect the convergence result differently between those two procedures? 3. Which procedure would be more appropriate for my model? Could anyone shed some lights on this? Thanks a lot!
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