- Paperback: 472 pages
- Publisher: SAGE Publications, Inc; 2 edition (November 29, 2010)
- Language: English
- ISBN-10: 141297514X
- ISBN-13: 978-1412975148
- Product Dimensions: 7 x 1 x 9.8 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #99,598 in Books (See Top 100 in Books)
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An R Companion to Applied Regression 2nd Edition
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"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R."--Robert W. Hayden
-The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R.---Robert W. Hayden
"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R." (Robert W. Hayden)
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The book has three particularly salient features: (1) the first 150 pages are a very nicely encapsulated introduction to R with hands-on examples that highlight many of the things that are covered in more detail later in the book. If you want to learn R and do regression models, this material is the perfect background. (2) the remainder of the book presents those models in more depth, building up piece by piece and with clear social science examples. Of special note is the attention to regression diagnostics -- which are often mentioned in other texts but not presented in such a practical way as here, with clearly worked examples and interpretations. (3) much of the content relies on the authors' R package "car", which provides a great set of tools for regression models, especially plotting, confidence interval estimation, and model diagnostics.
As an experienced R user, I enjoyed the book more than I expected: it taught me some very useful things about diagnostic tools, and demonstrated the "car" [companion to applied regression] package in a convincing way. I've known about car for years, but hadn't used it much; now I expect to use it regularly, especially for plotting.
Although the book calls itself a "companion" to other texts, it is actually self-contained if you already understand the basics of regression models in general. It avoids mathematical exegesis and focuses instead on exactly how to get things done in R ... and even more importantly, on how to understand what R is doing, how to interpret the results and work with the resulting objects, and how to avoid common problems. I'm going to start highly recommending this text to others who are new to R.
When choosing a book that would refresh my knowledge about linear regression, I was thinking about three:
- Linear models with R, by J. Faraway
- Using R for Introductory Statistics, by J. Verzani and
- An R companion to Applied Regression, Fox et al
I have fixed on the Mr. Fox's book mainly because he is the author of the "car" package and I wanted to learn more about it, mainly in the context of diagnosing linear regression models.
The book has completely met my expectations with solid applied coverage of the following areas:
- Visual data inspection
- Linear regression models
- Generalized linear regression models
- Diagnostics of LM and GLM
I highly recommend this book for two peculiar features. First, it feels like the author builds his knowledge of linear regression together with the reader, thus greatly facilitating learning process (this fact indeed is not surprising as Mr. Fox has been a university professor for years!). Second, the book heavily uses "car" package, of which Mr. Fox is the developer. "Car" package, on the one hand, helps increase the quality of regression models one produces and, on the other, decreases time needed for building a model.
Elegant simplicity this book lacks.
R isn't a hard language to use. But this book makes using R far more complicated than I ever thought it was.
Maybe the simplest and best way to do a regression on dummy variables (for factors) is to just change to either 0 or 1?
I hate to say it, but I was little disappointed.
This book isn't a complete disaster. The authors seems to understand R and regression really well.
1)Very well written chapters on linear regression and plotting.
2)Nicely written statistical concepts.
1)Takes simple concepts (dummy variable etc) and make them more complex than they should be.
2)The code examples are far more complex than they should be. A good code uses simplest and cleanest approach.
However, be warned this book is pretty hard. Much of the coding is challenging. I have attempted to replicate some of it; And, often without success getting repeated "Error" messages. Another reviewer criticized the author for using more complicated coding than necessary. And, I concur with this assessment.