If you are fitting a logistic model to a categorical response, I strongly recommend that you use PROC LOGISTIC rather than PROC CATMOD regardless of whether your response has two levels or several levels that are ordered (ordinal) or unordered (nominal). LOGISTIC can fit the same model as CATMOD, but LOGISTIC is more efficient, easier to use (especially when you want predicted values), has more options specialized for logistic models such as effect selection (forward, backward, stepwise, all subsets), and produces odds ratios and ROC curves. If your response has several levels (is multinomial), PROC LOGISTIC fits the ordinal model by default. If it is unordered, use the LINK=GLOGIT option in the MODEL statement to fit a generalized logit model as in PROC CATMOD. See the discussion and examples in the LOGISTIC documentation: SAS 9.2: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/logistic_toc.htm SAS/STAT 9.22 (beginning in SAS 9.2 TS2M3): http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/logistic_toc.htm SAS 9.3: http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/logistic_toc.htm The OUTEST= data set produced by PROC LOGISTIC has parameter names using the variable and level names.
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