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This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. A unified model-building strategy for both linear and nonlinear models is presented and applied to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing. A strong emphasis is placed on the use of graphical displays at the various phases of the model-building process, starting with exploratory plots of the data and concluding with diagnostic plots to assess the adequacy of a fitted model. Over 170 figures are included in the book. The NLME library for analyzing mixed-effects models in S and S-PLUS, developed by the authors, provides the underlying software for implementing the methods presented in the text, being described and illustrated in detail throughout the book. The balanced mix of real data examples, modeling software, and theory makes this book a useful reference for practitioners who use, or intend to use, mixed-effects models in their data analyses. It can also be used as a text for a one-semester graduate-level applied course in mixed-effects models. Researchers in statistical computing will also find this book appealing for its presentation of novel and efficient computational methods for fitting linear and nonlinear mixed-effects models. Jos C. Pinheiro has been a member of the technical staff in statistics research at Bell Laboratories since 1996. He received his Ph. D. in Statistics in 1994 from, and worked for two years in the Department of Biostatistics at the University of Wisconsin, Madison. The author of several articles in mixed-effects models, he is a memeber of the American Statistical Association and the Biometric Society. Douglas M. Bates is Professor of Statistics at the University of Wisconsin, Madison. He, with Donald G. Watts, is the author of "Nonlinear Regression Analysis and Its Applications", a Fellow of the American Statistical Association, and former chair of its Statistical Computing Section.
This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. A unified model-building strategy for both linear and nonlinear models is presented and applied to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing. A strong emphasis is placed on the use of graphical displays at the various phases of the model-building process, starting with exploratory plots of the data and concluding with diagnostic plots to assess the adequacy of a fitted model. Over 170 figures are included in the book. The NLME library for analyzing mixed-effects models in S and S-PLUS, developed by the authors, provides the underlying software for implementing the methods presented in the text, being described and illustrated in detail throughout the book. The balanced mix of real data examples, modeling software, and theory makes this book a useful reference for practitioners who use, or intend to use, mixed-effects models in their data analyses. It can also be used as a text for a one-semester graduate-level applied course in mixed-effects models. Researchers in statistical computing will also find this book appealing for its presentation of novel and efficient computational methods for fitting linear and nonlinear mixed-effects models. Jos C. Pinheiro has been a member of the technical staff in statistics research at Bell Laboratories since 1996. He received his Ph. D. in Statistics in 1994 from, and worked for two years in the Department of Biostatistics at the University of Wisconsin, Madison. The author of several articles in mixed-effects models, he is a memeber of the American Statistical Association and the Biometric Society. Douglas M. Bates is Professor of Statistics at the University of Wisconsin, Madison. He, with Donald G. Watts, is the author of "Nonlinear Regression Analysis and Its Applications", a Fellow of the American Statistical Association, and former chair of its Statistical Computing Section.
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Inside This Book
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Many common statistical models can be expressed as linear models that incorporate both fixed effects, which are parameters associated with an entire population or with certain repeatable levels of experimental factors, and random effects, which are associated with individual experimental units drawn at random from a population. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nlme model, nlme library, asymptotic regression model, quinidine data, variance covariate formula, optional character string specifying, ergometric experiment, nlme objects, fitted model object, gnls model, nime fit, orthodontic data, print brief information, relative precision factor, lme function, optional numeric value, lme fit, primary covariate, indomethacin data, optional numeric vector, extended linear model, variance function model, optional positive integer, augmented predictions, power variance function Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Std, Subject Structure, Wafer Structure, Corr Asym, Blocked Block, Composite Structure, Extending the Basic, Newton Raphson, Block Structure, Fitting Linear Mixed-Effects Models, Fitting Nonlinear Models, Monte Carlo, Plant Structure, Subject Data, Subject Subject, Caucasian Latin Black, Dialyzer Log-restricted-likelihood, Fitting Linear Models, Lot Structure, Mare Parameter, Mare Structure, Orthodont Log-restricted-likelihood, Thickness of Oxide Coating, Wafer Site New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Many common statistical models can be expressed as linear models that incorporate both fixed effects, which are parameters associated with an entire population or with certain repeatable levels of experimental factors, and random effects, which are associated with individual experimental units drawn at random from a population. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nlme model, nlme library, asymptotic regression model, quinidine data, variance covariate formula, optional character string specifying, ergometric experiment, nlme objects, fitted model object, gnls model, nime fit, orthodontic data, print brief information, relative precision factor, lme function, optional numeric value, lme fit, primary covariate, indomethacin data, optional numeric vector, extended linear model, variance function model, optional positive integer, augmented predictions, power variance function Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Std, Subject Structure, Wafer Structure, Corr Asym, Blocked Block, Composite Structure, Extending the Basic, Newton Raphson, Block Structure, Fitting Linear Mixed-Effects Models, Fitting Nonlinear Models, Monte Carlo, Plant Structure, Subject Data, Subject Subject, Caucasian Latin Black, Dialyzer Log-restricted-likelihood, Fitting Linear Models, Lot Structure, Mare Parameter, Mare Structure, Orthodont Log-restricted-likelihood, Thickness of Oxide Coating, Wafer Site New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 37
books:
See all 37 books this book cites
- Random Number Generation and Monte Carlo Methods (Statistics and Computing) by James E. Gentle in Front Matter, and Back Matter
- Numerical Linear Algebra for Applications in Statistics by James E. Gentle in Front Matter, and Back Matter
- Elements of Statistical Computing: NUMERICAL COMPUTATION by R. A. Thisted in Back Matter
- Analysis of Repeated Measures (Monographs on Statistics and Applied Probability) by Martin J. Crowder in Back Matter
- Statistics for Spatial Data (Wiley Series in Probability and Statistics) by Noel A. C. Cressie in Back Matter
See all 37 books this book cites
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Beta (What's this?)Mixed Effects Models in S and S-Plus
Correct publication date: 2000.
Created on Jan 17, 2007, last edited on Jan 17, 2007.
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