Statistical Models in S (Chapman & Hall Computer Science Series) (Hardcover)

~ John M. Chambers (Author), Trevor J. Hastie (Editor) "This book is about data and statistical models that try to explain data..." (more)
Key Phrases: fitted model object, gam object, same named components, Multiple R-squared, Temp Conc Cat, Weight Disp (more...)

Editorial Reviews

Product Description

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data. --This text refers to an out of print or unavailable edition of this title.

Product Details

• Hardcover: 500 pages
• Publisher: Chapman & Hall; 1 edition (October 1991)
• Language: English
• ISBN-10: 0412052911
• ISBN-13: 978-0412052910
• Product Dimensions: 9.8 x 6.5 x 1.5 inches
• Shipping Weight: 2.3 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #5,445,945 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

21 of 24 people found the following review helpful:

5.0 out of 5 stars This is "the white book", an essential S-PLUS reference., March 29, 2000

By A Customer

S programmers refer to this as "the white book", and it is a key reference for understanding the methods implemented in several of S-PLUS' high-end statistical functions, including 'lm()', predict()', 'design()', 'aov()', 'glm()', 'gam()', 'loess()', 'tree()', 'burl.tree()', 'nls()' and 'ms()'.

It's apparently out of print, but it shouldn't be.

Even with the recent arrival of S-PLUS releases that incorporate S version 4 and many of the ideas discussed in "the green book" (<<Programming with Data>>, also by John Chambers), this classic S reference is an indispensable tool for the serious statistician. It needs to be reissued--with a white cover, of course.

Here are the titles of the chapters, for reference:

1. An Appetizer

2. Statistical Models

3. Data for Models

4. Linear Models

5. Analysis of Variance: Designed Experiments

6. Generalized Linear Models

7. Generalized Additive Models

8. Local Regression Models

9. Tree-Based Models

10. Nonlinear Models

A. Classes and Methods: Object-oriented Programming in S

B. S Functions and Classes

References

Index

7 of 7 people found the following review helpful:

5.0 out of 5 stars Simply the Best (for those who want to know what they're doing), August 16, 2006

By	chipd (Maui, HI United States) - See all my reviews

This review is from: Statistical Models in S (Hardcover)

If you really want to know what you're doing when you use S, buy this book. Don't waste your money on a book like Venables and Ripley -- you will be sorely dissappointed, unless you just want a large collections of example calls to canned S routines. The authors of the present book, on the other hand, are Chambers and Hastie of AT&T (where S was invented), and they clearly understand the importance of detailed explanations of the theory underlying the S functions they describe. Just as important, in my opinion, they also describe the algorithms used by these functions. These two components are missing from other books (like the popular Venables and Ripley) but they are critical in order to know -- and be able to explain and justify to others -- how and why your statistical analyses were performed and what the results really mean. Any other was of doing statistics (e.g. throwing canned procedures at your data and seeing what pretty graphs and figures you can produce) is meaningless.

3 of 3 people found the following review helpful:

5.0 out of 5 stars Statistical Models in S, February 1, 2009

By	Murray M. Cooper (Richland, MI, 49083) - See all my reviews (REAL NAME)

This review is from: Statistical Models in S (Hardcover)

I am in the process of learning R, the open source implementation of the S language. This book is one of the classics describing the original S language. While some small parts, of this book, are now out of date, it remains a great source, of information about the design and use of S and by extension, of R.