SAS for Mixed Models

Walter W. Stroup, George A. Milliken, Elizabeth A. Claassen, Russell D. Wolfinger

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 two-way analysis of variance, regression, and intermediate-level use of SAS.

This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics:

• Random-effect-only and random-coefficients models


• Multilevel, split-plot, multilocation, and repeated measures models


• Hierarchical models with nested random effects


• Analysis of covariance models


• Generalized linear mixed models

This book is part of the SAS Press program.

Sujets

• Repeated measures design
• Best linear unbiased prediction
• Random effects model
• Fixed effects model
• Hierarchical linear modeling
• Degrees of freedom
• Linear model
• Design of experiments
• Matrix

Informations

• Publié par: SAS Institute
• Date de parution: 12 décembre 2018
• Nombre de lectures: 1
• EAN13: 9781635261523
• Langue: English
• Poids de l'ouvrage: 19 Mo

Extrait

The correct bibliographic citation for this manual is as follows: Stroup, Walter W., George A. Milliken, Elizabeth A. Claassen, and Russell D. Wolfinger . 2018. SAS for Mixed Models: Introduction and Basic Applications . Cary, NC: SAS Institute Inc.
SAS for Mixed Models: Introduction and Basic Applications
Copyright 2018, SAS Institute Inc., Cary, NC, USA
978-1-63526-135-6 (Hardcopy) 978-1-63526-154-7 (Web PDF) 978-1-63526-152-3 (epub) 978-1-63526-153-0 (mobi)
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December 2018
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Contents

About This Book
Dedication and Acknowledgments
Chapter 1: Mixed Model Basics
1.1 Introduction
1.2 Statistical Models
1.3 Forms of Linear Predictors
1.4 Fixed and Random Effects
1.5 Mixed Models
1.6 Typical Studies and Modeling Issues That Arise
1.7 A Typology for Mixed Models
1.8 Flowcharts to Select SAS Software to Run Various Mixed Models
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 Two-Way 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
3.1 Introduction
3.2 Comparison of Two Treatments
3.3 Comparison of Several Means: Analysis of Variance
3.4 Comparison of Quantitative Factors: Polynomial Regression
3.5 Mean Comparisons in Factorial Designs
3.6 Summary
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 Split-Plot 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 Non-Estimability
5.9 Summary
Chapter 6: Random Effects Models
6.1 Introduction: Descriptions of Random Effects Models
6.2 One-Way Random Effects Treatment Structure: Influent Example
6.3 A Simple Conditional Hierarchical Linear Model: An Example
6.4 Three-Level Nested Design Structure: An Example
6.5 A Two-Way 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 One-Way Fixed Effects Treatment Structure with Simple Linear Regression Models
7.3 One-Way Treatment Structure in an RCB Design Structure-Equal Slopes Model: An Example
7.4 One-Way Treatment Structure in an Incomplete Block Design Structure: An Example
7.5 One-Way Treatment Structure in a BIB Design Structure: An Example
7.6 One-Way Treatment Structure in an Unbalanced Incomplete Block Design Structure: An Example
7.7 Multilevel or Split-Plot Design with the Covariate Measured on the Large-Size Experimental Unit or Whole Plot: An Example
7.8 Summary
Chapter 8: Analysis of Repeated Measures Data
8.1 Introduction
8.2 Mixed Model Analysis of Data from Basic Repeated Measures Design: An Example
8.3 Covariance Structures
8.4 PROC GLIMMIX Analysis of FEV1 Data
8.5 Unequally Spaced Repeated Measures: An Example
8.6 Summary
Chapter 9: Best Linear Unbiased Prediction (BLUP) and Inference on Random Effects
9.1 Introduction
9.2 Examples Motivating BLUP
9.3 Obtainment of BLUPs in the Breeding Random Effects Model
9.4 Machine-Operator Two-Factor Mixed Model
9.6 Matrix Notation for BLUP
9.7 Summary
Chapter 10: Random Coefficient Models
10.1 Introduction
10.2 One-Way Random Effects Treatment Structure in a Completely Randomized Design Structure: An Example
10.3 Random Student Effects: An Example
10.4 Repeated Measures Growth Study: An Example
10.5 Prediction of the Shelf Life of a Product
10.6 Summary
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 O-Ring 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
13.1 Introduction
13.2 Two Examples Illustrating Generalized Linear Mixed Models with Complex Data
13.3 Example 1: Split-Plot Experiment with Count Data
13.4 Example 2: Repeated Measures Experiment with Binomial Data
Chapter 14: Power, Precision, and Sample Size II: General Approaches
14.1 Introduction
14.2 Split Plot Example Suggesting the Need for a Follow-Up Study
14.3 Precision and Power Analysis for Planning a Split-Plot 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 Non-Gaussian 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
15.1 Introduction
15.2 Troubleshooting
15.3 Residuals
15.4 Influence Diagnostics
15.5 Two Diagnostic Plots Useful for Non-Gaussian Data
15.5 Summary
Appendix A: Linear Mixed Model Theory
A.1 Introduction
A.2 Matrix Notation
A.3 Formulation of the Mixed Model
A.4 Estimating Parameters, Predicting Random Effects
A.5 Statistical Properties
A.6 Model Selection
A.7 Inference and Test Statistics
Appendix B: Generalized Linear Mixed Model Theory
B.1 Introduction
B.2 Formulation of the Generalized Linear Model
B.3 Formulation of the Generalized Linear Mixed Model
B.4 Conditional versus Marginal Mo