Mixed Effects Models in S and S-Plus
Mixed Effects Models in S and S-Plus

Mixed Effects Models in S and S-Plus

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Springer Verlag

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978038798957

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$89.95

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Product Specifications
Product NameMixed Effects Models in S and S-Plus
ManufacturerSpringer Verlag
Product Number MPN0387989579
Retail Price $89.95
UPC978038798957
Specifications 
TitleMixed Effects Models in S and S-Plus
ISBN0387989579
Author(s)Jose C. Pinheiro, Douglas M. Bates
Release Date2000-05-15
FormatHardcover
Num of Pages528
Num. of Items1
EAN9780387989570
Weight0.5 lbs.

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Reviews
5 Star Rating  "great book, important topic in biostatistics especially"2008-01-24
- Reviewed By User: AQ7ZQWXAYT8HZ
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.

 
5 Star Rating  "excellent text, very useful in statistical analysis in clinical trials"2008-01-24
- Reviewed By User: AQ7ZQWXAYT8HZ
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.
 
2 Star Rating  "Good in some ways but"2006-08-07
- Reviewed By charlespaxton
It is difficult to understand who this book is written for. The authors are clearly clever guys but strangely the book does little to explain the command structure for modelling in the authors' own package! It would be useful to explain the models in the context of random effects/nested modelling in conventional ANOVA for newbies. Please authors if you do a second edition show how really simple models are programmed.

A good example of its beginner unfriendliness is the first example(!) to have a vector within a dataframe of exactly the same name. Not a good idea in a text book. You would have thought the editor would have had something to say about that.
 
5 Star Rating  "Very good textbook for (non)linear mixed models in R"2006-07-25
- Reviewed By tch28
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.
 
4 Star Rating  "As someone who just learn R"2006-01-19
- Reviewed By fallingmaple
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.
 
5 Star Rating  "R and S, The best in statistical analysis"2004-01-16
- Reviewed By argenis9
The book has excelent presentation (theory and practical), overall a lot applications with R (my favorite)...If you want to be update in applied statistics, in my opinion, you should have it...
 
5 Star Rating  "well written account of mixed models with SPlus software"2001-04-22
- Reviewed By statman13
Mixed effects linear models are very useful particularly in medical research (e.g. device or drug trials). Pinheiro and Bates provide comprehensive cover 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.

 
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