- Series: Chapman & Hall/CRC Texts in Statistical Science (Book 63)
- Hardcover: 240 pages
- Publisher: Chapman and Hall/CRC; 1 edition (July 26, 2004)
- Language: English
- ISBN-10: 1584884258
- ISBN-13: 978-1584884255
- Product Dimensions: 6.2 x 0.8 x 9.2 inches
- Shipping Weight: 1 pounds
- Average Customer Review: 15 customer reviews
- Amazon Best Sellers Rank: #1,124,818 in Books (See Top 100 in Books)
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Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
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One danger with applied books such as this is that they become recipe lists of the kind 'press this key to get that result.' This is not so with Faraway's book. Throughout, it gives plenty of insight on what is going on, with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen
I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models
I find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught.
-Journal of the Royal Statistical Society
Overall, Linear Models with R is well written and, given the increasing popularity of R, it is an important contribution.
-Technometrics, Vol. 47, No. 3, August 2005
There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysis The text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations.
-Short Book Reviews of the International Statistical
The book is very comprehensibly written and can therefore be recommended for beginners in linear models. It is clearly and simply explained how to use R and which packages are necessary to analyze linear models. All in all, this book is recommendable as a textbook for computational linear regression courses and therefore for students and lecturers, but also for applied statisticians who want to get started on regression analysis using the software R.
Dr Faraway uses many examples and graphical procedures to illustrate the methods. This is a great strength of the book. Linear Models with R is one of several books appearing to make R more accessible by bringing together functions from a number of packages and illustrating their use. From this perspective alone it is an important contribution. I feel this book does a nice job of describing the methods available in linear modeling and illustrating the realistic implementation of these methods in a careful data analysis.
-Statistics in Medicine, 2006
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Some reviewers have criticized Linear Models with R for having incomplete R code. There is some basis for this criticism. There were examples that I could not understand from just looking at the text. However, the complete code for the book is available from Julian Faraway's web site. Looking at the downloaded code and running it helped fill in the gaps.
I am a big fan of R and have been using it pretty intensively for over a year. As a platform for applied mathematics and statistics, R is unmatched, especially because its free. However, the documentation that comes with R frequently leaves a lot to be desired.
One way to look at Linear Models with R is as the manual that should have come with the R linear modeling packages, but didn't. I liked this book enough that I also bought Julian Faraway's Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models , but I have not worked through this book yet.
I've read a good portion of the book, reading the first several chapters and skipping around more on a need-to-know level for the other topics. Below is a list of the chapters:
5. Problems with Predictors
6. Problems with the Error
8. Variable Selection
9. Shrinkage Methods
10. Statistical Strategy and Model Uncertainty
11. Insurance Redlining -- A Complete Example.
12. Missing Data
13. Analysis of Covariance
14. One-Way Analysis of Variance
15. Factorial Designs
16. Block Designs
I will inevitably be buying what is like the second volume of this book, "Extending the Linear Model with R" as needs arise.
This book, just as its author, is thin, clean and concise. It does not always explain and reveal things in full detail. If you are smart, like to think and explore data using R/Splus, or have a good mentor, this book fits you well. Its discussion on principle component analysis, in my opinion, is a little bit weak.
The book is excellent in the sense that it guides the reader through a number of fairly useful techniques using linear models and generalized linear models. I adopted the strategy of starting with the first chapter and working each example in R as the author presents it. I've learned a lot about how to use R doing this. It took me a while to get used to the "unified" linear model idea- viz., regression and ANOVA are based on the same linear model. The book has a number of excellent examples that demonstrate the power of R and show the reader how to exploit various library functions.
Overall I like the book. It is an excellent introduction to how to use R in one's linear model analyses. It is long on "here, type this in and you'll get....." and short on the theory/principles behind each technique.
One aspect of R I have not yet cracked- how do I (easily) specify a linear model that includes only interactions up to level 2 or 3? I currently enter the formula as Y~A*B+A*C + ... and so on. Must be an easier way.
The discussions on robust methods are good, but (understandably) short. Use of the trimmed least squares and quantile regression techniques as well as a discussion of using bootstrap methods to get a confidence interval are, again presented as "here, type this in and..." At least this book got me headed in the right direction. There is also a 2- or 3-page summary of basic R commands to help the neophyte get started with R.
A good book, but could be better with some presentation of the theory behind each of the techniques presented.
Most recent customer reviews
This book assumes you're pretty proficient in R, there are no solutions to the problems anywhere so you can't...Read more