Note that the presence of the CLASS statement does not make the model a multinomial model. In PROC LOGISTIC, the model is a multinomial model if the response variable has more than two distinct values. Further, a multinomial model can be a ordinal or nominal multinomial model depending on whether the multiple levels of the response have a natural ordering or not. PROC LOGISTIC fits an ordinal model by default (using cumulative logits) when there is a multilevel response. If the response level are not ordered, then the LINK=GLOGIT option in the MODEL statement can be specified to fit a nominal multinomial model.
Now to your question. As with the binary logistic model, you can use the SCALE=NONE and AGGREGATE options in the MODEL statement to obtain Pearson and deviance statistics which are valid goodness of fit tests only if there is sufficient replication within the subpopulations defined by the distinct covariate patterns in the data. For purposes of comparing competing models you can use the R-square statistic available with the RSQUARE option in the MODEL statement. Also for comparing models, you could use the AIC and SC statistics which are provided by default. Of course, you can always save predicted probabilities from the model using the PREDPROBS=INDIVIDUAL option in the OUTPUT statement. Similarly, you can score a new set of data and save predicted probabilities using the SCORE statement. See the example titled "Scoring Data Sets" in the LOGISTIC documentation.
