Hi Reeza, many thanks! the design matrix is the same in both PROCs. But the log is not clean since my dataset is not large enough. I think if I get more data, the convegence will be ok. Do you think it is the reseaon? Does it mean if I get larger dataset, then the predicted probabilities in both PROCs will be the same? But it is strange that the OR estimates are the same in both PROCs even though there are warnings from both. log from PROC LOGISTIC: NOTE: PROC LOGISTIC is modeling the probability that AVAL='Response'. WARNING: There is possibly a quasi-complete separation of data points. The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. WARNING: The model does not have a GLM parameterization. This parameterization is required for the LSMEANS, LSMESTIMATE, and SLICE statement. These statements are ignored. log from PROC GENMOD: NOTE: PROC GENMOD is modeling the probability that AVAL='Response'. WARNING: The negative of the Hessian is not positive definite. The convergence is questionable. WARNING: The procedure is continuing but the validity of the model fit is questionable. WARNING: The specified model did not converge. NOTE: The Pearson chi-square and deviance are not computed since the AGGREGATE option is not specified. WARNING: Negative of Hessian not positive definite.
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