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 SAS for Mixed Models, Second Edition, the comprehensive mixed models guide for data analysis, completely revised and updated for SAS 9 by authors Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger. 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; and 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.
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"With an abundance of new material and a thorough updating of material from the first edition, SAS for Mixed Models, Second Edition will be of inordinate interest to those of us engaged in the modeling of messy continuous and categorical data. It contains several new chapters, and its printed format makes this a much more readable version than its predecessor. We owe the authors a tip of the hat for providing such an invaluable compendium." --Timothy G. Gregoire, J. P. Weyerhaeuser Professor of Forest Management, School of Forestry and Environmental Studies, Yale University
"This extensively updated second edition of SAS for Mixed Models will be an essential reference for anyone involved in mixed modeling. A decade of experience has been incorporated into each chapter with three completely new chapters on mixed model diagnostics, power calculations, and Bayesian approaches. The book is filled with practical advice for dealing with important issues that come up with real data, such as avoiding common pitfalls in modeling, correctly interpreting output, troubleshooting nonlinear mixed model fitting and choosing from among the multiple SAS procedures/macros now available.
The already distinguished group of authors has been further enhanced with the addition of Oliver Schabenberger, the developer of the new GLIMMIX procedure and an expert in spatial modeling and mixed model diagnostics. The procedures that are new since the first edition, NLMIXED and GLIMMIX, are extensively covered. Examples of new graphics and new output from the GLIMMIX procedure are scattered throughout the book because of their usefulness in both standard linear mixed modeling and generalized linear mixed modeling." --Cliff Pereira, Department of Statistics, Oregon State University
With an abundance of new material and a thorough updating of material from the first edition, SAS for Mixed Models, Second Edition will be of inordinate interest to those of us engaged in the modeling of messy continuous and categorical data. It contains several new chapters, and its printed format makes this a much more readable version than its predecessor. We owe the authors a tip of the hat for providing such an invaluable compendium. --Timothy G. Gregoire, J. P. Weyerhaeuser Professor of Forest Management, School of Forestry and Environmental Studies, Yale University
"This extensively updated second edition of SAS for Mixed Models will be an essential reference for anyone involved in mixed modeling. A decade of experience has been incorporated into each chapter with three completely new chapters on mixed model diagnostics, power calculations, and Bayesian approaches. The book is filled with practical advice for dealing with important issues that come up with real data, such as avoiding common pitfalls in modeling, correctly interpreting output, troubleshooting nonlinear mixed model fitting and choosing from among the multiple SAS procedures/macros now available.
The already distinguished group of authors has been further enhanced with the addition of Oliver Schabenberger, the developer of the new GLIMMIX procedure and an expert in spatial modeling and mixed model diagnostics. The procedures that are new since the first edition, NLMIXED and GLIMMIX, are extensively covered. Examples of new graphics and new output from the GLIMMIX procedure are scattered throughout the book because of their usefulness in both standard linear mixed modeling and generalized linear mixed modeling." --Cliff Pereira, Department of Statistics, Oregon State University
With an abundance of new material and a thorough updating of material from the first edition, SAS for Mixed Models, Second Edition will be of inordinate interest to those of us engaged in the modeling of messy continuous and categorical data. It contains several new chapters, and its printed format makes this a much more readable version than its predecessor. We owe the authors a tip of the hat for providing such an invaluable compendium. --Timothy G. Gregoire, J. P. Weyerhaeuser Professor of Forest Management, School of Forestry and Environmental Studies, Yale University
About the Author
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, 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, 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, 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, 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.
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1 of 1 people found the following review helpful:
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This review is from: SAS for Mixed Models, Second Edition (Paperback)
This book is excellent for showing the basic theory and practical examples of mixed models and how they can be easily implemented using SAS. I think there should have been a chapter on other non-statistical issues concerning how models can be implemented, such as scoring new observations with a mixed-effects regression model, or data preparation for these models, etc. Other than that, it is an excellent book and use it when the need for mixed-models arises in workplace.
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The book looks new and disc is usable. Shipped fast.It's on time. I am very satisfied about the condition of the book.
7 of 11 people found the following review helpful:
5.0 out of 5 stars
A neccesary book for mixed model analysis,
This review is from: SAS for Mixed Models, Second Edition (Paperback)
I am a Ph.D. student, and I work with longitudinal and hierarchical data. I bought this book recently and I have only had a look at it. I like it very much. This book presents mixed model methodology in the setting of numerous applications and many examples are included from several applications areas. It is a useful book even to people with no muck knowledge of analysis of variance an regression analysis. You do not need to read the book from cover to cover. This book is certainly applied.
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Inside This Book
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First Sentence:
Data sets presented in this book come from three types of sources: (1) designed experiments, (2) sample surveys, and (3) observational studies. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Estimate Standard Error, Least Squares Means, Tests of Fixed Effects Effect Num, Results Output, Res Log Likelihood, Simple Effect Level, Contrasts Label Num, Class Level Information, Estimate Std Err Pred, Dimensions Covariance Parameters, Number of Observations Used, Iteration History Iteration Evaluations, Itl Intercept, Source Type, Corrected Total, Matrix Row Effect, Null Model Likelihood Ratio Test, High-Flux Hemodialyzer Models, Itl Adjustment Adj, Max Obs Per Subject, Results Figure, Generalized Chi-Square, Res Log Like Criterion, Residual Variance Method Profile Fixed Effects, Asymptotic Std New!
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Data sets presented in this book come from three types of sources: (1) designed experiments, (2) sample surveys, and (3) observational studies. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Estimate Standard Error, Least Squares Means, Tests of Fixed Effects Effect Num, Results Output, Res Log Likelihood, Simple Effect Level, Contrasts Label Num, Class Level Information, Estimate Std Err Pred, Dimensions Covariance Parameters, Number of Observations Used, Iteration History Iteration Evaluations, Itl Intercept, Source Type, Corrected Total, Matrix Row Effect, Null Model Likelihood Ratio Test, High-Flux Hemodialyzer Models, Itl Adjustment Adj, Max Obs Per Subject, Results Figure, Generalized Chi-Square, Res Log Like Criterion, Residual Variance Method Profile Fixed Effects, Asymptotic Std New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 70
books:
See all 70 books this book cites
- Multiple Comparisons and Multiple Tests (Text and Workbook Set) by Peter H. Westfall in Back Matter (1), and Back Matter (2)
- Experimental Designs by William Gemmel Cochran on page 488, and Back Matter
- Cartoon Guide to Statistics by Larry Gonick in Back Matter
- Output Delivery System: The Basics by Lauren E. Haworth in Back Matter
- Visualizing Categorical Data by Michael Friendly in Back Matter
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