Mixed-Effects Models in S and S-PLUS (Statistics and Computing)

This paperback edition is a reprint of the 2000 edition. 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 areincluded in the book. The NLME package for analyzing mixed-effects models in R 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 using 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. José C. Pinheiro is a Senior Biometrical Fellow at Novartis Pharmaceuticals, having worked at Bell Labs during the time this book was produced. He has published extensively in mixed-effects models, dose finding methods in clinical development, and other areas of biostatistics. Douglas M. Bates is Professor of Statistics at the University of Wisconsin-Madison. He is the author, with Donald G. Watts, of __Nonlinear Regression Analysis and Its Applications,__ a Fellow of the American Statistical Association, and a former chair of the Statistical Computing Section. 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. The authors present a unified model-building strategy for both models and apply this to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing. Much 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. 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. This balanced mix of real data examples, modeling software, and theory makes the 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. Content: Front Matter....Pages i-xvi Front Matter....Pages 1-1 Linear Mixed-Effects Models: Basic Concepts and Examples....Pages 3-56 Theory and Computational Methods for Linear Mixed-Effects Models....Pages 57-96 Describing the Structure of Grouped Data....Pages 97-132 Fitting Linear Mixed-Effects Models....Pages 133-199 Extending the Basic Linear Mixed-Effects Model....Pages 201-270 Front Matter....Pages 272-272 Nonlinear Mixed-Effects Models: Basic Concepts and Motivating Examples....Pages 273-304 Theory and Computational Methods for Nonlinear Mixed-Effects Models....Pages 305-336 Fitting Nonlinear Mixed-Effects Models....Pages 337-414 Back Matter....Pages 415-528 Linear Mixed-Effects Models Linear Mixed-Effects Models: Basic Concepts and Examples Theory and Computational Methods for Linear Mixed-Effects Models Describing the Structure of Grouped Data Fitting Linear Mixed-Effects Models Extending the Basic Linear Mixed-Effects Model Nonlinear Mixed-Effects Models Nonlinear Mixed-Effects Models: Basic Concepts and Motivating Examples Theory and Computational Methods for Nonlinear Mixed-Effects Models Fitting Nonlinear Mixed-Effects Models. Offers 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. This book presents a unified model-building strategy for both models and applies this to the analysis of over 20 real datasets from a various areas. The balanced mix of real data examples, modeling software, and theory makes this book a useful reference for practitioners who use mixed-effects models. Researchers in statistical computing will also learn novel and efficient computational methods for fitting linear and non-linear mixed effects models. 172 illus.