If you want to contrast the 0 and 2-4 levels of response, then you really just want a binary response model. The easiest way would be to create a dichotomized version of your response and fit a binary logistic model to it. However, if you are determined to do it in the context of the multinomial model, then you can use the NLEstimate macro to estimate a suitable expression for the desired function. Using the schools and instruction styles example in the note I referred to, suppose you want to estimate the relative risk that contrasts the self and team styles vs. the class style in school 1. That is a dichotomization of the response levels like what you want. Writing this in terms of the response probabilities, suppose you want to estimate the relative risk (p_self+p_team)/p_class in school 1. Writing this in terms of the parameters of the multinomial model, the function to estimate is
exp(intercept_self+school1_self) + exp(intercept_team+school1_team)
which can then be used in the FDATA= data set used by NLEstimate:
[P(self,S1)+P(team,S1)]/P(class,S1) | exp(b_p1+b_p3)+exp(b_p2+b_p4)
You mentioned getting different OR estimates… The NLMeans macro with options=ratio will estimate relative risks as stated in the note I referred to, not odds ratios. If you want to estimate odds ratios, you will need to estimate suitable expressions in the NLEstimate macro.
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