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
Live Instructor Dates