Modern Applied Statistics with S

Rating:
Summary: An excellent introduction to S
Review: "Modern Applied Statistics with S" provides a comprehensive introduction to the use of S in either of its implementations (S-PLUS or R). I have found it to be an indespensible resource when I use R. The authors work through a variety of major topics in statistics and demonstrate the use of S with sample code and datasets. To be clear, however, this book was not written as an introduction to statistics. You'll need to look elsewhere for such a book. Consider "Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance" by Underwood or "Applied Linear Statistical Models" by Neter et al. for two excellent but completely different approaches.

Rating:
Summary: Excellent
Review: A worthy update. The authors are matter-of-fact and straightforward. I appreciate their terse style, the broad coverage, and the many examples. It's also good that they're starting to split programming material off into its own monograph (S Programming), making this book all the more appropriate for learning, although not quite beginning, statisticians. Their considerable contribution in software is also very much appreciated.

I do not agree at all with the reviewer who chided them for including R; I say so much the better for it. I very much hope that they will continue to do so.

Rating:
Summary: Excellent
Review: A worthy update. The authors are matter-of-fact and straightforward. I appreciate their terse style, the broad coverage, and the many examples. It's also good that they're starting to split programming material off into its own monograph (S Programming), making this book all the more appropriate for learning, although not quite beginning, statisticians. Their considerable contribution in software is also very much appreciated.

I do not agree at all with the reviewer who chided them for including R; I say so much the better for it. I very much hope that they will continue to do so.

Rating:
Summary: R deserves the coverage it gets here
Review: Another reviewer wrote "I suspect most practicioners use S+". He should have been at the UserR! 2004 conference in Vienna this past March, with 500 or so enthusiastic R users including many from big industry (financial, pharmaceutical). And Ripley is the number-one contributor to the R Help mailing list by a long way. So it is completely appropriate that R is so prominant. Many of us appreciate open source not only for its cost ($0) but also its transparency. The reviewer should take another look at R.

As for the book, it is my data anlysis bible. It gets me started in a correct direction, with very well-explained and worked out examples, which I then adapt to my own datasets. The writing couldn't be clearer, and the references to primary sources as well as non-computational statistics texts I have found to be excellent. This is the one book to own if you are more than a beginner.

Rating:
Summary: An excellent application of statistics with S
Review: I am an experienced S-Plus user. I found the 4th ed. of the popular book very helpful (and practical). The authors added many libraries to the packaged S-Plus, which is very generous. I would give it a five-star ranking, but I found the various comments on R really disturbing (I suspect most practioners use S-Plus instead of R). Maybe the authors should put everything related to R into separated boxes (or in a separate chapter). Overall, it's a great book and I would like to recommend it to anyone interested in computational statistics. Note: Basic knowledge of S and some advanced training statistics are assumed. Good for graduate students in statistics/finance, or experienced statisticians/financial engineers. For those without sufficient training in statistics, try the S-Plus Guide to Statistics I & II first.

Rating:
Summary: Excelent statistics reference
Review: This book is not only THE reference on statistical techniques using S-Plus/R, but also a very good statistical reference on applied statistics. As stated by the authors on the Preface, almost all the material on the book is what is covered in the M.Sc. in Applied Statistics at the Oxford University. The book covers the most used topics in Applied Statistics currently, including Linear Models, GLM, Non-linear and Smooth Regression, Tree-based methods, Random and Mixed Effect Models, Multivariate Analaysis, Classification, Survival Analysis, Time Series Analysis and Spatial Statistics.