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Statistical Analysis with the GLIMMIX Procedure

This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models. Skills Gained analyze binomial data with random effects fit a Poisson regression model and a...

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$2,000 USD GSA  $1,707.69
Course Code GLIM42
Available Formats Classroom, Virtual

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

Skills Gained

  • 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 Can Benefit

  • 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.

Course Details

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
  • modeling multivariate responses
  • dealing with convergence problems
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