This course teaches you how to analyze linear mixed models using the MIXED procedure. A brief introduction to analyzing generalized linear mixed models using the GLIMMIX procedure is also included.
Learn How To
analyze data (including binary data) with random effects fit random coefficient models and hierarchical linear models analyze repeated measures data obtain and interpret the best linear unbiased predictions perform residual and influence diagnostic analysis address convergence issues.Who Should Attend
Statisticians, experienced data analysts, and researchers with sound statistical knowledge
Prerequisites
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 2: ANOVA and Regression course or completed a graduate-level course on general linear models have an understanding of generalized linear models and their analysis.;Exposure to mixed models and matrix algebra will enhance your understanding of the material. Some experience manipulating SAS data sets and producing graphs using SAS statistical graphing procedures is also recommended.SAS Products Covered
SAS/STAT
Course Outline
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 testsExamples of Mixed Models in Some Designed Experimentsfitting a linear mixed model for two-way mixed modelsfitting a linear mixed model for nested mixed models fitting a linear mixed model for split-plot designs fitting a linear mixed model for crossover designsExamples of Mixed Models with Covariatesfitting analysis of covariance models with random effects performing random coefficient regression analysis conducting hierarchical linear modelingBest Linear Unbiased Predictionexplaining 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 procedureRepeated Measures Analysisdiscussing issues on repeated measures analysis, including modeling covariance structure analyzing repeated measures data using the four-step process with the MIXED procedureMixed Models Residual Diagnostics and Troubleshootingperforming residual and influence diagnostics for linear mixed models troubleshooting convergence problemsAdditional 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 methodsIntroduction to Generalized Linear Mixed Models and Nonlinear Mixed Modelsdiscussing the situations where generalized linear mixed models and nonlinear mixed models analysis are needed performing the analysis for generalized linear mixed models using the GLIMMIX procedure
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