Book description
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in twoway analysis of variance, regression, and intermediatelevel use of SAS.
This book expands coverage of mixed models for nonnormal data and mixedmodelbased precision and power analysis, including the following topics: Randomeffectonly and randomcoefficients models
 Multilevel, splitplot, multilocation, and repeated measures models
 Hierarchical models with nested random effects
 Analysis of covariance models
 Generalized linear mixed models
Table of contents
 About This Book
 Dedication and Acknowledgments
 Chapter 1: Mixed Model Basics

Chapter 2: Design Structure I: Single Random Effect
 2.1 Introduction
 2.2 Mixed Model for a Randomized Block Design
 2.3 The MIXED and GLIMMIX Procedures to Analyze RCBD Data
 2.4 Unbalanced TwoWay Mixed Model: Examples with Incomplete Block Design
 2.5 Analysis with a Negative Block Variance Estimate: An Example
 2.6 Introduction to Mixed Model Theory
 2.7 Summary
 Chapter 3: Mean Comparisons for Fixed Effects

Chapter 4: Power, Precision, and Sample Size I: Basic Concepts
 4.1 Introduction
 4.2 Understanding Essential Background for Mixed Model Power and Precision
 4.3 Computing Precision and Power for CRD: An Example
 4.4 Comparing Competing Designs I—CRD versus RCBD: An Example
 4.5 Comparing Competing Designs II—Complete versus Incomplete Block Designs: An Example
 4.6 Using Simulation for Precision and Power
 4.7 Summary

Chapter 5: Design Structure II: Models with Multiple Random Effects
 5.1 Introduction
 5.2 Treatment and Experiment Structure and Associated Models
 5.3 Inference with Factorial Treatment Designs with Various Mixed Models
 5.4 A SplitPlot Semiconductor Experiment: An Example
 5.5 A Brief Comment about PROC GLM
 5.6 Type × Dose Response: An Example
 5.7 Variance Component Estimates Equal to Zero: An Example
 5.8 A Note on PROC GLM Compared to PROC GLIMMIX and PROC MIXED: Incomplete Blocks, Missing Data, and Spurious NonEstimability
 5.9 Summary

Chapter 6: Random Effects Models
 6.1 Introduction: Descriptions of Random Effects Models
 6.2 OneWay Random Effects Treatment Structure: Influent Example
 6.3 A Simple Conditional Hierarchical Linear Model: An Example
 6.4 ThreeLevel Nested Design Structure: An Example
 6.5 A TwoWay Random Effects Treatment Structure to Estimate Heritability: An Example
 6.6 Modern ANOVA with Variance Components
 6.7 Summary

Chapter 7: Analysis of Covariance
 7.1 Introduction
 7.2 OneWay Fixed Effects Treatment Structure with Simple Linear Regression Models
 7.3 OneWay Treatment Structure in an RCB Design Structure—Equal Slopes Model: An Example
 7.4 OneWay Treatment Structure in an Incomplete Block Design Structure: An Example
 7.5 OneWay Treatment Structure in a BIB Design Structure: An Example
 7.6 OneWay Treatment Structure in an Unbalanced Incomplete Block Design Structure: An Example
 7.7 Multilevel or SplitPlot Design with the Covariate Measured on the LargeSize Experimental Unit or Whole Plot: An Example
 7.8 Summary
 Chapter 8: Analysis of Repeated Measures Data
 Chapter 9: Best Linear Unbiased Prediction (BLUP) and Inference on Random Effects
 Chapter 10: Random Coefficient Models

Chapter 11: Generalized Linear Mixed Models for Binomial Data
 11.1 Introduction
 11.2 Three Examples of Generalized Linear Mixed Models for Binomial Data
 11.3 Example 1: Binomial ORing Data
 11.4 Generalized Linear Model Background
 11.5 Example 2: Binomial Data in a Multicenter Clinical Trial
 11.6 Example 3: Binary Data from a Dairy Cattle Breeding Trial
 11.7 Summary

Chapter 12: Generalized Linear Mixed Models for Count Data
 12.1 Introduction
 12.2 Three Examples Illustrating Generalized Linear Mixed Models with Count Data
 12.3 Overview of Modeling Considerations for Count Data
 12.4 Example 1: Completely Random Design with Count Data
 12.5 Example 2: Count Data from an Incomplete Block Design
 12.6 Example 3: Linear Regression with a Discrete Count Dependent Variable
 12.7 Blocked Design Revisited: What to Do When Block Variance Estimate is Negative
 12.8 Summary
 Chapter 13: Generalized Linear Mixed Models for Multilevel and Repeated Measures Experiments

Chapter 14: Power, Precision, and Sample Size II: General Approaches
 14.1 Introduction
 14.2 Split Plot Example Suggesting the Need for a FollowUp Study
 14.3 Precision and Power Analysis for Planning a SplitPlot Experiment
 14.4 Use of Mixed Model Methods to Compare Two Proposed Designs
 14.5 Precision and Power Analysis: A Repeated Measures Example
 14.6 Precision and Power Analysis for NonGaussian Data: A Binomial Example
 14.7 Precision and Power: Example with Incomplete Blocks and Count Data
 14.8 Summary
 Chapter 15: Mixed Model Troubleshooting and Diagnostics
 Appendix A: Linear Mixed Model Theory
 Appendix B: Generalized Linear Mixed Model Theory
 References
 Index
Product information
 Title: SAS for Mixed Models
 Author(s):
 Release date: December 2018
 Publisher(s): SAS Institute
 ISBN: 9781635261523
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