Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS

Abstract

This chapter describes how to use the genreg macro for adaptive logistic regression modeling as described in Chap. 8 and its generated output in the special case of univariate dichotomous or polytomous outcomes. Example analyses are provided for modeling means and dispersions for mercury in fish categorized into the dichotomous levels of high and low and into the polytomous levels of high, medium, and low in terms of weight and length of the fish and the river in which they were caught. Adaptive ordinal and multinomial regression for polytomous outcomes are demonstrated. Residual analyses based on continuous predictors, like weight and length, of dichotomous and polytomous outcomes are better conducted using grouped data. Formulations and example analyses are provided for grouped-data residual analyses of both dichotomous and polytomous outcomes.