Mixed-Effects Models in Sand S-PLUS
By: by José C. Pinheiro, Douglas M. Bates
Contributors: Bates, Douglas M., author
Series: Statistics and Computing; Statistics and Computing
Publisher: Springer New York
Published: 2000
Place of publication: New York, NY
Language: eng
ISBN-13: 9781441903181; 9781441903174
Subjects: Statistics; Mathematical statistics; Statistical Theory and Methods
Description: 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.
Table of contents: title: Linear Mixed-Effects Models; title: Linear Mixed-Effects Models: Basic Concepts and Examples; title: Theory and Computational Methods for Linear Mixed-Effects Models; title: Describing the Structure of Grouped Data; title: Fitting Linear Mixed-Effects Models; title: Extending the Basic Linear Mixed-Effects Model; title: Nonlinear Mixed-Effects Models; title: Nonlinear Mixed-Effects Models: Basic Concepts and Motivating Examples; title: Theory and Computational Methods for Nonlinear Mixed-Effects Models; title: Fitting Nonlinear Mixed-Effects Models.