Mixed Effects Models in S and S-Plus [Hardcover]

Jose C. Pinheiro (Author), Douglas M. Bates (Author)

Book Description

Publication Date: May 5, 2000 | ISBN-10: 0387989579 | ISBN-13: 978-0387989570

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.

Editorial Reviews

From the Back Cover

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.

--This text refers to the Paperback edition.

Product Details

• Hardcover: 528 pages
• Publisher: Springer (May 5, 2000)
• Language: English
• ISBN-10: 0387989579
• ISBN-13: 978-0387989570
• Product Dimensions: 9.2 x 6.5 x 1.2 inches
• Shipping Weight: 2 pounds
• Average Customer Review: 4.2 out of 5 stars  See all reviews (6 customer reviews)
• Amazon Best Sellers Rank: #1,282,899 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

31 of 32 people found the following review helpful:

5.0 out of 5 stars excellent text, very useful in statistical analysis in clinical trials, January 24, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Mixed Effects Models in S and S-Plus (Hardcover)

Mixed effects linear models are very useful particularly in medical research (e.g. device or drug trials). Pinheiro and Bates provide comprehensive coverage of both linear and nonlinear mixed effects models with many applications. Implementation is illustrated using the S programming language and the software package SPlus.

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.

10 of 10 people found the following review helpful:

5.0 out of 5 stars Very good textbook for (non)linear mixed models in R, July 25, 2006

By 

C. Tu (Lincoln, Nebraska United States) - See all my reviews
(REAL NAME)   

This review is from: Mixed Effects Models in S and S-Plus (Hardcover)

Even though the title of this book is mixed effects models in S and S+ but this is a wonderful book for a person to learn mixed effect models in R. If you read this book carefully and also use the R to practice examples. Then you will get a lot from the learning process. Of course you should has a basic background in linear model before you read this book.
I strong recommend this book to whom needs nonlinear mixed models of longitudinal data in R.
Every statistician should has this book.

12 of 17 people found the following review helpful:

4.0 out of 5 stars As someone who just learn R, January 18, 2006

By 

Falling Maple (Boston, MA) - See all my reviews

Amazon Verified Purchase

This review is from: Mixed Effects Models in S and S-Plus (Hardcover)

At first sight, there are a lot of SPlus/R commands in the book which one may expect to learn a lot about using nlme. However, I found there is a lack in explanation of the command, if not missing. For e.g., in Chapter 1, the book talks about nested classficification models and gave the command in Splus/R, with the model equation right in front of me, I still can't figure out why in the command ...... random=list(Dog=~day,Side=~1) .... can't figure out the logic of this command in relation to the equation. I know this is not an introductory book for R, but a lot of time, when we want to use R or Splus the first time, it's not b'cos we want to do simple statistics, so a bit more explanation of the commands will be helpful, rather than following the commands blindly. Furthermore, I'm not even talking about R programming. Having said that, I still want to emphasize it is a good book written for the topic and package.