SAS for Mixed Models,9781590475003

SAS for Mixed Models

by ; ; ; ;
Edition: 2nd
ISBN13: 9781590475003
ISBN10: 1590475003
Format: Paperback
Pub. Date: 2006-03-30
Publisher(s): Sas Inst
List Price: $129.59

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Summary

The indispensable, up-to-date guide to mixed models using SAS?. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures in this valuable edition of the comprehensive mixed models guide for data analysis, completely revised and updated for SAS?9. The theory underlying the models, the forms of the models for various applications, and a wealth of examples from different fields of study are integrated in the discussions of these models: random effect only and random coefficients models split-plot, multilocation, and repeated measures models hierarchical models with nested random effects analysis of covariance models spatial correlation models generalized linear mixed models nonlinear mixed models Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered. Includes a free CD-ROM with example SAS code!

Author Biography

Ramon C. Littell Ramon C. Littell, Ph.D., Professor of Statistics at the University of Florida, is the coauthor of several books, including SAS System for Regression, Third Edition, and SAS for Linear Models, Fourth Edition. He has worked with SAS software since 1986. George A. Milliken George A. Milliken, Ph.D., Professor of Statistics at Kansas State University, has been using SAS software since 1974 and has extensive experience with the design and analysis of experiments using mixed models by incorporating the GLM, MIXED, GLIMMIX, and NLMIXED procedures. Walter W. Stroup Walter W. Stroup, Ph.D., Professor and Chair of the Department of Statistics at the University of Nebraska, is the coauthor of SAS for Linear Models, Fourth Edition. He has been using SAS software since 1981. Russell D. Wolfinger Russell D. Wolfinger, Ph.D., is the Director of Scientific Discovery and Genomics at SAS Institute, where he has worked since 1989. Before leading SAS scientific efforts, he authored the MIXED, MULTTEST, KDE, and NLMIXED procedures in SAS/STAT. Oliver Schabenberger Oliver Schabenberger, Ph.D., is a Senior Research Statistician at SAS Institute and has been using SAS software since 1991. He maintains and develops mixed model software and is the author of the GLIMMIX procedure.

Table of Contents

Preface ix
Introduction
1(16)
Types of Models That Produce Data
1(1)
Statistical Models
2(2)
Fixed and Random Effects
4(2)
Mixed Models
6(1)
Typical Studies and the Modeling Issues They Raise
7(4)
A Typology for Mixed Models
11(2)
Flowcharts to Select SAS Software to Run Various Mixed Models
13(4)
Randomized Block Designs
17(40)
Introduction
18(1)
Mixed Model for a Randomized Complete Blocks Design
18(4)
Using Proc mixed to Analyze RCBD Data
22(20)
Introduction to Theory of Mixed Models
42(2)
Example of an Unbalanced Two-Way Mixed Model: Incomplete Block Design
44(12)
Summary
56(1)
Random Effects Models
57(36)
Introduction: Descriptions of Random Effects Models
58(6)
Example: One-Way Random Effects Treatment Structure
64(11)
Example: A Simple Conditional Hierarchical Linear Model
75(6)
Example: Three-Level Nested Design Structure
81(7)
Example: A Two-Way Random Effects Treatment Structure to Estimate Heritability
88(3)
Summary
91(2)
Multi-factor Treatment Designs with Multiple Error Terms
93(66)
Introduction
94(1)
Treatment and Experiment Structure and Associated Models
94(8)
Inference with Mixed Models for Factorial Treatment Designs
102(11)
Example: A Split-Plot Semiconductor Experiment
113(17)
Comparison with Proc GLM
130(5)
Example: Type x Dose Response
135(13)
Example: Variance Component Estimates Equal to Zero
148(6)
More on Proc GLM Compared to Proc Mixed: Incomplete Blocks, Missing Data, and Estimability
154(2)
Summary
156(3)
Analysis of Repeated Measures Data
159(46)
Introduction
160(3)
Example: Mixed Model Analysis of Data from Basic Repeated Measures Design
163(11)
Modeling Covariance Structure
174(24)
Example: Unequally Spaced Repeated Measures
198(4)
Summary
202(3)
Best Linear Unbiased Prediction
205(38)
Introduction
206(1)
Examples of BLUP
206(4)
Basic Concepts of BLUP
210(2)
Example: Obtaining BLUPs in a Random Effects Model
212(7)
Example: Two-Factor Mixed Model
219(7)
A Multilocation Example
226(8)
Location-Specific Inference in Multicenter Example
234(7)
Summary
241(2)
Analysis of Covariance
243(74)
Introduction
244(1)
One-Way Fixed Effects Treatment Structure with Simple Linear Regression Models
245(6)
Example: One-Way Treatment Structure in a Randomized Complete Block Design Structure---Equal Slopes Model
251(12)
Example: One-Way Treatment Structure in an Incomplete Block Design Structure---Time to Boil Water
263(9)
Example: One-Way Treatment Structure in a Balanced Incomplete Block Design Structure
272(9)
Example: One-Way Treatment Structure in an Unbalanced Incomplete Block Design Structure
281(5)
Example: Split-Plot Design with the Covariate Measured on the Large-Size Experimental Unit or Whole Plot
286(11)
Example: Split-Plot Design with the Covariate Measured on the Small-Size Experimental Unit or Subplot
297(11)
Example: Complex Strip-Plot Design with the Covariate Measured on an Intermediate-Size Experimental Unit
308(7)
Summary
315(2)
Random Coefficient Models
317(26)
Introduction
317(3)
Example: One-Way Random Effects Treatment Structure in a Completely Randomized Design Structure
320(6)
Example: Random Student Effects
326(4)
Example: Repeated Measures Growth Study
330(11)
Summary
341(2)
Heterogeneous Variance Models
343(70)
Introduction
344(1)
Example: Two-Way Analysis of Variance with Unequal Variances
345(9)
Example: Simple Linear Regression Model with Unequal Variances
354(12)
Example: Nested Model with Unequal Variances for a Random Effect
366(8)
Example: Within-Subject Variability
374(19)
Example: Combining Between- and Within-Subject Heterogeneity
393(9)
Example: Log-Linear Variance Models
402(9)
Summary
411(2)
Mixed Model Diagnostics
413(24)
Introduction
413(2)
From Linear to Linear Mixed Models
415(9)
The Influence Diagnostics
424(2)
Example: Unequally Spaced Repeated Measures
426(9)
Summary
435(2)
Spatial Variability
437(42)
Introduction
438(1)
Description
438(2)
Spatial Correlation Models
440(2)
Spatial Variability and Mixed Models
442(5)
Example: Estimating Spatial Covariance
447(10)
Using Spatial Covariance for Adjustment: Part 1, Regression
457(3)
Using Spatial Covariance for Adjustment: Part 2, Analysis of Variance
460(11)
Example: Spatial Prediction---Kriging
471(7)
Summary
478(1)
Power Calculations for Mixed Models
479(18)
Introduction
479(1)
Power Analysis of a Pilot Study
480(3)
Constructing Power Curves
483(3)
Comparing Spatial Designs
486(3)
Power via Simulation
489(6)
Summary
495(2)
Some Bayesian Approaches to Mixed Models
497(28)
Introduction and Background
497(2)
P-Values and Some Alternatives
499(3)
Bayes Factors and Posterior Probabilities of Null Hypotheses
502(5)
Example: Teaching Methods
507(2)
Generating a Sample from the Posterior Distribution with the Prior Statement
509(2)
Example: Beetle Fecundity
511(13)
Summary
524(1)
Generalized Linear Mixed Models
525(42)
Introduction
526(1)
Two Examples to Illustrate When Generalized Linear Mixed Models Are Needed
527(2)
Generalized Linear Model Background
529(9)
From GLMs to GLMMs
538(4)
Example: Binomial Data in a Multi-center Clinical Trial
542(15)
Example: Count Data in a Split-Plot Design
557(9)
Summary
566(1)
Nonlinear Mixed Models
567(70)
Introduction
568(1)
Background on Proc Nlmixed
569(2)
Example: Logistic Growth Curve Model
571(16)
Example: Nested Nonlinear Random Effects Models
587(2)
Example: Zero-Inflated Poisson and Hurdle Poisson Models
589(6)
Example: Joint Survival and Longitudinal Model
595(12)
Example: One-Compartment Pharmacokinetic Model
607(16)
Comparison of Proc Nlmixed and the %Nlinmix Macro
623(2)
Three General Fitting Methods Available in the %Nlinmix Macro
625(4)
Troubleshooting Nonlinear Mixed Model Fitting
629(5)
Summary
634(3)
Case Studies
637(96)
Introduction
638(1)
Response Surface Experiment in a Split-Plot Design
639(4)
Response Surface Experiment with Repeated Measures
643(7)
A Split-Plot Experiment with Correlated Whole Plots
650(9)
A Complex Split Plot: Whole Plot Conducted as an Incomplete Latin Square
659(8)
A Complex Strip-Split-Split-Plot Example
667(7)
Unreplicated Split-Plot Design
674(10)
23 Treatment Structure in a Split-Plot Design with the Three-Way Interaction as the Whole-Plot Comparison
684(10)
23 Treatment Structure in an Incomplete Block Design Structure with Balanced Confounding
694(5)
Product Acceptability Study with Crossover and Repeated Measures
699(17)
Random Coefficients Modeling of an AIDS Trial
716(11)
Microarray Example
727(6)
Appendix 1 Linear Mixed Model Theory
733(24)
Introduction
734(1)
Matrix Notation
734(1)
Formulation of the Mixed Model
735(7)
Estimating Parameters, Predicting Random Effects
742(9)
Statistical Properties
751(1)
Model Selection
752(2)
Inference and Test Statistics
754(3)
Appendix 2 Data Sets
757(24)
Randomized Block Designs
759(1)
Random Effects Models
759(2)
Analyzing Multi-level and Split-Plot Designs
761(1)
Analysis of Repeated Measures Data
762(2)
Best Linear Unbiased Prediction
764(1)
Analysis of Covariance
765(3)
Random Coefficient Models
768(1)
Heterogeneous Variance Models
769(2)
Mixed Model Diagnostics
771(1)
Spatial Variability
772(1)
Some Bayesian Approaches to Mixed Models
773(1)
Generalized Linear Mixed Models
774(1)
Nonlinear Mixed Models
775(1)
Case Studies
776(5)
References 781(14)
Index 795

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