From GLM to GLIMMIX-Which Model to Choose?

Abstract

The purpose of this paper is to investigate several SAS procedures that are used in linear predictive models in SAS/Stat. The primary focus will be on the correct choice of model given the designated outcome variable, and the combination of input variables. Procedures to be discussed include GLM, LOGISTIC, GENMOD, MIXED, and GLIMMIX. PROC GLIMMIX is a relatively new SAS procedure, although it has been available as a macro for some time. There are three main types of variables used in linear models: nominal, ordinal, and interval. Nominal is defined as categorical (such as gender); ordinal is defined as categorical that can be ordered from least to most (such as employee evaluation rank); interval data can define ratios. While all of the models discussed can include all three types of input variables, the model choice is different if the outcome variable is interval or nominal. Another consideration for model choice is whether the input variables are fixed effects or random effects. Fixed effects are definitive, and will not change regardless of the sample data collection. Random effects can change when the experiment is replicated. Examples of random effects include subjects in a drug study, choice of items to compare between retail stores for market basket price differences, and classrooms in an education study. Examples will be discussed.