Mixed-Effects Models in S and S-PLUS
(Sprache: Englisch)
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...
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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. 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. Over 170 figures are included in the book.
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 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 has been a member of the technical staff in statistics research at Bell Laboratories since 1996. He received his Ph.D. in Statistics 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 member of the American Statistical Association and the Biometric Society. 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 its Statistical Computing Section.
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 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 has been a member of the technical staff in statistics research at Bell Laboratories since 1996. He received his Ph.D. in Statistics 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 member of the American Statistical Association and the Biometric Society. 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 its Statistical Computing Section.
Inhaltsverzeichnis zu „Mixed-Effects Models in S and S-PLUS “
Linear Mixed-Effects Theory and Computational Methods for LME Models Structure of Grouped Data Fitting LME Models Extending the Basic LME Model Nonlinear Mixed-Effects Theory and Computational Methods for NLME Models Fitting NLME Models
Autoren-Porträt von José Pinheiro, Douglas Bates
Mixed Effects Models involve measurements made over time on anindividual in an experiment. This book presents the most recent
techniques for analyzing this type of data in the statistical software program S-PLUS. It will be of interest to researchers and graduate
students in statistics, biostatistics, and epidemiology.
Bibliographische Angaben
- Autoren: José Pinheiro , Douglas Bates
- 2001, Corr. pr., 528 Seiten, 172 Abbildungen, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, New York
- ISBN-10: 0387989579
- ISBN-13: 9780387989570
- Erscheinungsdatum: 02.04.2002
Sprache:
Englisch
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