Mixed Models Analyses Using SAS (AGLM92)
Duration
3 days
Description
This course teaches you how to analyze linear and
generalized mixed models using the MIXED and GLIMMIX procedures,
respectively.
Learn how to:
- analyze data (including binary data) with random effects
- fit random coefficient models and hierarchical linear models
- analyze repeated measures data
- perform mixed model diagnostics
- obtain and interpret the best linear unbiased predictions
- deal with convergence issues
Prerequisite Skills
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 II: ANOVA and Regression course or completed a graduate-level course on general linear models.
Exposure to mixed 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 Topics
Introduction to Mixed Models
- identifying fixed and random effects
-
describing linear mixed model equations and assumptions
- fitting a linear mixed model for a randomized complete block design using the MIXED procedure
- writing CONTRAST and ESTIMATE statements to perform custom hypothesis tests
- being aware of estimation methods for variance components, fixed effect parameter estimates and inferences, and denominatior degrees of freedom
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
- analyzing repeated measures data using the two-stage approach with the MIXED procedure
Diagnostics and Troubleshooting
- performing mixed model diagnostics, discussing issues associated with unbalanced data, and nonconvergence problems
Introduction to Nonlinear Mixed Models
- discussing the situations where nonlinear mixed models analysis are needed
- performing the analysis using generalized linear mixed models and the GLIMMIX procedure
Booking
Please contact the Education Team at SAS for the latest information on all SAS courses or to put your name on our specialised course waiting list.
