- Taschenbuch: 548 Seiten
- Verlag: Springer; Auflage: Softcover reprint of the original 1st ed. 2000 (4. Oktober 2013)
- Sprache: Englisch
- ISBN-10: 147578144X
- ISBN-13: 978-1475781441
- Größe und/oder Gewicht: 15,5 x 3,1 x 23,5 cm
- Durchschnittliche Kundenbewertung: 2 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 884.342 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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Mixed-Effects Models in S and S-Plus (Statistics and Computing) (Englisch) Taschenbuch – 4. Oktober 2013
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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 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. Jose 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. -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.
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This is a useful book for using the nlme and lme4 packages in R, as it covers the theory of mixed effects models and provides practical examples of their analysis in S. The code can be used in R, as I have been doing, although the output may differ somewhat from that provided in the book.
If you wish to learn about mixed effects models, and in particular if you are using R, I recommend this book.
Bates is an expert on nonlinear regression and hence the emphasis on the nonlinear models as well as the linear ones. These models are very useful for handling repeated measures data with missing observations. Such data often arise in clinical trials and these models have been used to do the intnt to treat analysis that is often required in regulatory submissions to the FDA, Also some variables are quite naturally modelled as a random effects component in the model.The specific clinical site for investigators in a multi-site trial is one common example.
I strong recommend this book to whom needs nonlinear mixed models of longitudinal data in R.
Every statistician should has this book.