- Hardcover: 528 pages
- Publisher: Springer; 1st ed. 2000. Corr. 3rd printing edition (1 May 2002)
- Language English
- ISBN-10: 0387989579
- ISBN-13: 978-0387989570
- Product Dimensions: 23.4 x 16.4 x 3 cm
- Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: 1,145,658 in Books (See Top 100 in Books)
- See Complete Table of Contents
Mixed-Effects Models in S and S-PLUS
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Product Description
Product Description
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.
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First Sentence
Many common statistical models can be expressed as linear models that incorporate both fixed effects, which are parameters associated with an entire population or with certain repeatable levels of experimental factors, and random effects, which are associated with individual experimental units drawn at random from a population. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Many common statistical models can be expressed as linear models that incorporate both fixed effects, which are parameters associated with an entire population or with certain repeatable levels of experimental factors, and random effects, which are associated with individual experimental units drawn at random from a population. Read the first page
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Customer Reviews
Most Helpful Customer Reviews
9 of 9 people found the following review helpful
Format:Hardcover
This book is a life saver... if you were struggling with the poorly structured examples in the S Plus 2000 Guide to Stats manuals, then this is a breath of pure fresh air. Clearly written, good level of detail, complex theory well referenced and partitioned nicely into different chapters from the step by step explanations of how to apply the techniques. Excellent, rich examples, nice tricks and clear code explanations for displaying and assessing data and models... This text has allowed me to open the box xontaining mixed effect models and start to use the tools to analyse my data, correctly. Brilliant.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
6 reviews
31 of 32 people found the following review helpful
excellent text, very useful in statistical analysis in clinical trials
24 Jan 2008
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.
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
Very good textbook for (non)linear mixed models in R
25 July 2006
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.
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
As someone who just learn R
18 Jan 2006
Format:Hardcover|Amazon 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.
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