This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models.

• analyze binomial data with random effects
• 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

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)

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.

This course addresses SAS/STAT software.

• introduction to generalized linear mixed models
• introduction to the GLIMMIX procedure using logistic regression with random effects

• Poisson regression with random effects
• an example of beta regression
• repeated measures data with discrete response
• introduction to radial smoothing (self-study)

• 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
• modeling multivariate responses
• dealing with convergence problems