Need to Return Your Textbook Rental?
Mixed-Effects Models in S and S-PLUS and over one million other books are available for Amazon Kindle. Learn more

Sorry, this item is not available in
Image not available for
Color:
Image not available

To view this video download Flash Player

 


or
Sign in to turn on 1-Click ordering
Sell Us Your Item
For a $21.52 Gift Card
Trade in
More Buying Choices
Have one to sell? Sell yours here
Start reading Mixed-Effects Models in S and S-PLUS on your Kindle in under a minute.

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

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

José Pinheiro , Douglas Bates
4.3 out of 5 stars  See all reviews (7 customer reviews)

Buy New
$90.38 & FREE Shipping. Details
Rent
$32.29
In Stock.
Ships from and sold by Amazon.com. Gift-wrap available.
In Stock.
Rented by RentU and Fulfilled by Amazon.
Want it Monday, July 14? Choose One-Day Shipping at checkout. Details
Free Two-Day Shipping for College Students with Amazon Student

Formats

Amazon Price New from Used from
Kindle Edition $83.20  
Hardcover --  
Paperback $90.38  
Shop the New Digital Design Bookstore
Check out the Digital Design Bookstore, a new hub for photographers, art directors, illustrators, web developers, and other creative individuals to find highly rated and highly relevant career resources. Shop books on web development and graphic design, or check out blog posts by authors and thought-leaders in the design industry. Shop now

Book Description

April 17, 2009 1441903178 978-1441903174 1st ed. 2000. 2nd printing 2009
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.

Frequently Bought Together

Mixed-Effects Models in S and S-PLUS (Statistics and Computing) + Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)
Price for both: $153.28

Buy the selected items together


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.


Product Details

  • Series: Statistics and Computing
  • Paperback: 530 pages
  • Publisher: Springer; 1st ed. 2000. 2nd printing 2009 edition (April 17, 2009)
  • Language: English
  • ISBN-10: 1441903178
  • ISBN-13: 978-1441903174
  • Product Dimensions: 1.2 x 6.1 x 9 inches
  • Shipping Weight: 1.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #665,936 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

4.3 out of 5 stars
(7)
4.3 out of 5 stars
Share your thoughts with other customers
Most Helpful Customer Reviews
31 of 32 people found the following review helpful
Format: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.
Was this review helpful to you?
10 of 10 people found the following review helpful
By C. Tu
Format: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.
Comment | 
Was this review helpful to you?
12 of 17 people found the following review helpful
4.0 out of 5 stars As someone who just learn R January 18, 2006
Format:Hardcover|Verified Purchase
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.
Comment | 
Was this review helpful to you?
5.0 out of 5 stars thesis December 13, 2012
Format:Paperback|Verified Purchase
this book is the basic concept for mixed effect model which is really helpful for the first time learning how to use R for LME model.
Comment | 
Was this review helpful to you?


Forums

There are no discussions about this product yet.
Be the first to discuss this product with the community.
Start a new discussion
Topic:
First post:
Prompts for sign-in
 



Look for Similar Items by Category