fit a Poisson regression model and a beta regression model with and without random effects

analyze repeated measures data with discrete outcomes

perform post-processing analysis

use the EFFECT statement to define customized model effects

jointly model multivariate responses with different distributions

deal with convergence issues.

Who Should Attend

Analysts, statisticians, and researchers

Prerequisites

Before attending this course, you should have taken;

SAS Programming 1: Essentials or have equivalent SAS programming experience

Mixed Models Analyses Using SAS or have equivalent experience analyzing linear mixed models using the MIXED procedure

Categorical Data Analysis Using Logistic Regression or have equivalent experience analyzing categorical response data.;Previous exposure to matrix algebra will enhance your understanding of the material.

SAS Products Covered

SAS/STAT

Course Outline

Introduction to Generalized Linear Mixed Models and the GLIMMIX Procedure

introduction to generalized linear mixed models

introduction to the GLIMMIX procedure using logistic regression with random effects

Applications Using the GLIMMIX Procedure

Poisson regression with random effects

an example of beta regression

repeated measures data with discrete response

introduction to radial smoothing (self-study)

GLIMMIX Procedure Topics

estimation methods used in PROC GLIMMIX

processing models by subjects

the FIRSTORDER adjustment to the KR degrees of freedom estimation method

covariance matrix diagnostics

constructed effects using the EFFECT statement

processing models by subjects

comparison of the GLIMMIX procedure and the NLMIXED procedure

comparison of the GLIMMIX procedure and the GENMOD procedure