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Mixed Models Analyses Using SAS - AGLM92

This course teaches you how to analyse linear mixed models using PROC MIXED. A brief introduction to analysing generalised linear mixed models using PROC GLIMMIX is also included.

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3 days - Classroom

Learn how to

  • analyse data (including binary data) with random effects
  • fit random coefficient models and hierarchical linear models
  • analyse repeated measures data
  • obtain and interpret the best linear unbiased predictions
  • perform residual and influence diagnostic anlaysis
  • deal with convergence issues.

Who should attend

Statisticians, experienced data analysts, and researchers with sound statistical knowledge


Before attending this course, you should

  • know how to create and manage SAS data sets
  • have experience performing analysis of variance using the GLM procedure of SAS/STAT software
  • have completed and mastered the Statistics the Applying Statistical Concepts using SAS9.2® course or completed a graduate-level course on general linear models.

Exposure to matrix algebra will enhance your understanding of the material. Some experience manipulating SAS data sets and producing graphs using SAS/GRAPH software is also recommended.

Course Contents

Introduction to Mixed Models

  • identifying fixed and random effects
  • describing linear mixed model equations and assumptions
  • fitting a linear mixed model for a randomised complete block design using the MIXED procedure
  • writing CONTRAST and ESTIMATE statements to perform custom hypothesis tests

Examples of Mixed Models in Some Designed Experiments

  • fitting a linear mixed model for two-way mixed models
  • fitting a linear mixed model for nested mixed models
  • fitting a linear mixed model for split-plot designs
  • fitting a linear mixed model for crossover designs

Examples of Mixed Models with Covariates

  • fitting analysis of covariance models with random effects
  • performing random coefficient regression analysis
  • conducting hierarchical linear modeling

Best Linear Unbiased Prediction

  • explaining BLUPs and EBLUPs
  • producing parameter estimates associated with the fixed effects and random effects
  • explaining the difference between LSMEANS and EBLUPs
  • computing LSMEANS and EBLUPs using the MIXED procedure

Repeated Measures Analysis

  • discussing issues on repeated measures analysis, including modeling covariance structure
  • analysing repeated measures data using the four-step process with the MIXED procedure

Mixed Models Residual Diagnostics and Troubleshooting

  • performing residual and influence diagnostics for linear mixed models
  • troubleshooting convergence problems

Additional Information about Linear Mixed Models (Self-Study)

  • discussing issues associated with unbalanced data, data with empty cells, estimation and inference of variance parameters, and different denominator degrees of freedom estimation methods

Introduction to Generalised Linear Mixed Models and Nonlinear Mixed Models

  • discussing the situations where generalised linear mixed models and nonlinear mixed models analysis are needed
  • performing the analysis for generalised linear mixed models using the GLIMMIX procedure

Software Addressed

This course addresses the following software product(s): SAS/STAT.

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