These messages occur when some parameters of the model are detected to be infinite. Parameters of the model often become infinite when there are zeros in the table that PROC CATMOD analyzes, that is, when the table is sparse. The table will typically be sparse when any of the predictors are continuous such that there are values that occur only once. To see the table that CATMOD is analyzing, use the FREQ option in the MODEL statement. By reducing the number of effects in the MODEL statement and/or the number of levels per variable (both of which will reduce the number of parameters in the model), you can reduce the size of the table and remove these zeros. Looking at the table should help you find the least disruptive way to remove the most zeros. If any variable in the MODEL statement has many levels (variables in the DIRECT statement, if used, are likely candidates here), try categorizing it to reduce the number of levels.

Note that if the model you are trying to fit is a binary or multinomial logistic model, you should use PROC LOGISTIC instead of PROC CATMOD. PROC LOGISTIC is more efficient, easier to use (especially when you want to obtain predicted values), has more options specialized for logistic models (odds ratios, ROC curves, etc.), and offers effect selection methods (forward, backward, stepwise, all subsets). Note that parameters can become infinite for the same reason in PROC LOGISTIC, but that procedure tries to detect this situation and reports it as separation. See SAS Note 22599 for more information on separation and suggestions for addressing the problem. If the total count in the table is small, you may be able to use exact logistic regression via the EXACT statement in PROC LOGISTIC.

If you are trying to fit a log-linear model, you can fit it as a Poisson model in PROC GENMOD. Specify the variable of cell counts as the response and specify the DIST=POISSON option in the MODEL statement.