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Modern Applied Statistics With S-Plus (Statistics and Computing)

Download Ebook Modern Applied Statistics With S-Plus (Statistics and Computing)

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Product details

Series: Statistics and Computing

Hardcover: 548 pages

Publisher: Springer-Verlag; 2nd edition (August 1997)

Language: English

ISBN-10: 9780387982144

ISBN-13: 978-0387982144

ASIN: 0387982140

Product Dimensions:

1.2 x 6.5 x 9.8 inches

Shipping Weight: 1.7 pounds (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

22 customer reviews

Amazon Best Sellers Rank:

#2,539,185 in Books (See Top 100 in Books)

The printed book (which I own) deserves 5 stars as a comprehensive introduction to the S/R language and statistics.The one star concerns explicitly the Kindle edition. In short: The rendering of formula and even (plain text!) S/R code is a scandal.Formula and S/R code are included as bad resolution pictures and NOT rendered professionally in HTML5. They appear in pale grey and cannot be zoomed!Springer has demonstrated with another Kindle book I purchased (Bapat RB: Linear Algebra and Linear Models) that they are capable of producing a professional Kindle version.I purchased the Kindle edition in addition to the printed one, just to have it around when I need to look things up.The same issue is valid for the Springer Kindle versions of:Dalgaard P: Introductory Statistics with RZuur AF et al: Mixed Effects Models and Extensions in Ecology with RI will repeat this statement in reviews for the above mentioned books.When I have found out how to contact costumer service at Springer I will formally demand a refund or an update.Do not buy the Kindle edition.

This is *the* book to have on S+/R. It provides excellent value for its price (indeed, any price): it is concise, broad, informative. All the same, I think it would be useful to identify intended audience for this book (in my view). First, the book is not for novices in Statistics. You'll learn how to fit generalized linear models in the language, not how and why to apply such models properly. To this end, there are plenty of specific monographies, and the majority of them use R for examples. Just to name a few, Friedman, Hastie, Tibshirani, Harrell, Faraway employ R. Also, this book assume some basic knowledge of programming. R is a more elegant language than Matlab and Thinking in R becomes very natural after some practice. But I have not seen so far a tutorial on "R as a first language". Summing up, this is a great book for undergraduates in Statistics/Engineering and up, who want a comprehensive, usable reference. My only criticism is that since 2002 there have been giant changes in the language. First, R is now the main implementation of the language, with S+ being an industry-supported variant. Second, the S4 object model is here to stay and grow, and is crying for a user-friendly introduction. Lastly, the number of packages is probably twenty times what it was in 2002. SVMs, ensemble methods, shrinkage, sparse representations *are* modern applied statistics, and are underrepresented in the book.Still, this is a must have for any applied statistician.

I started using R to do linear modeling and found that I was using 'library(MASS)' much of the time. MASS, it turns out, stands for Modern Applied Statistics with S. R is a free ware version of S-Plus. I assumed that R is simply S-Plus without the GUI. I was close, but not right. There are some minor differences. This book, written for S also addresses the use of R in the applications presented, and also notes differences between the two, when they exist. I am quite pleased with Venerables and Ripley's book; it presents much of the theoretical background as well as 'command line' code for doing the analyses presented in each chapter. The book assumes the reader has some background in statistics.The first five chapters are a brief overview of /introduction to S-Plus (or R). These chapters present enough information and examples to make the rest of the book fairly easy to work through.I got the book primarily to work design of experiments. The chapters on linear statistical models and general linear models were perfectly suited to my needs. Topics like factorial experiments, random and mixed effects, nested designs, partially balanced designs are covered. In addition, techniques of robust analysis and bootstrap methods are presented.The book covers many other areas - non-linear models, classification, time series, optimization.. I have not worked through any of these topics in the book.Overall I find Modern Applied Statistics with S to be an excellent book, invaluable if one is using R (I don't have S-Plus) as the vehicle for analyses.

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.

The "search inside this book" feature was not available when this review was posted. Hope it helps.CONTENTSIntroductionData ManipulationThe S LanguageGraphicsUnivariate StatisticsLinear Statistical ModelsGeneralized Linear ModelsNon-Linear and Smooth RegressionTree-Based MethodsRandom and Mixed EffectsExploratory Multivariate AnalysisClassificationSurvival AnalysisTime Series AnalysisSpatial StatisticsOptimizationImplementation-Specific DetailsThe S-PLUS GUIDatasets, Software and LibrariesReferencesIndex

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