Modern Applied Statistics with S

by W.N. Venables, B.D. Ripley

S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs, MARS, SOM and support vector machines are considered.

Genres: Mathematics, Science

510 pages, Hardcover

First published January 1, 2002

About the author

William Venables

Reviews

[Kelly · Rating 4 out of 5]

This is an very good resource for learning how to implement various statistical methods in R (which is based on S). The authors do provide some theoretical and mathematical explanation of the various methods they cover, which was helpful as a refresher, but I found it was not as effective for learning the basis of methods I didn't already know, especially because the authors go through things rather quickly. They have a wealth of examples from a wide range of fields, which is helpful for people coming to the book from different disciplines. However, I found that, once they produced statistical results for a given example, they didn't explain what those results would mean, even in cases in which they had gone into a fair bit of detail explaining the statistical questions they were analyzing the data to ask. Since this isn't a statistics textbook (as I said before), this is fair, but it would have been nice to get a little bit more explanation of some results, especially for techniques with which I was not as familiar.

All in all, though, a great resource when used in conjunction with a statistics textbook or course and the R help files, with some good programming examples, and some nice time-saving tips for using R. Anyone who isn't at least somewhat familiar with R already, though, should be warned that the book is a bit fast on the basics, so they won't be able to rely on this exclusively and will have to supplement with the R help documentation and / or a more introductory resource.

[Milza Lopez · Rating 5 out of 5]

First, I want to say (If you don't know it yet), this book even is not recent, is absolutely a basic manual for R, so don't let yourself be distracted by the title. There are lots of R functions that will lead you in it's references to this book, and this text is pure gold. Venables and Ripley are not only making easy to copy and paste scripts, but they are also giving great introductions, complete applications and every mathematical condition and function you can imagine to be related to the subjects.

Finally, to be clear, this book is really complete, easy to read and allows you to get a lot of self learning. I totally recomend it.

[Barry · Rating 3 out of 5]

I think this was the first book I bought on R, and it's certainly the most well-thumbed. The graphing examples are really quite good, but it's a really hard slog if you're new to R. Once you've gotten familiar, though, check it out if you're doing a lot of data visualization work.