the GLIMMIX Procedure for Generalized Linear Mixed Models"
A Presentation by Maura Stokes
This paper describes a new
SAS/STAT procedure for fitting models to non-normal or normal data with
correlations or nonconstant variability. PROC GLIMMIX extends the SAS
mixed model tools in a number of ways. For example, it
* models data from non-Gaussian distributions
* implements low-rank smoothing based on mixed models
* provides new features for LS-means comparisons and display
* enables you to use SAS programming statements to compute model
effects, or to define link and variance functions
* fits models to multivariate data in which observations do not all
have the same distribution or link
Applications of the GLIMMIX procedure include estimating trends in
disease rates, modeling counts or proportions over time in a clinical
trial, predicting probability of occurrence in time series and spatial
data, and joint modeling of correlated binary and continuous data.
This presentation described generalized linear mixed models and how to
use the GLIMMIX procedure for estimation, inference, and prediction.
Examples from several application areas were presented.
Maura Stokes is an R & D Director in the statistical software
development division at SAS. She is the lead author of the book
Categorical Data Analysis Using the SAS System, and she has taught and
written about categorical data analysis for over twenty years.
Cecilia Yee giving
a Certificate of Appreciation from the Detroit Chapter