Statistical Models in S
Statistical Models in S Books
Product Description
Statistical Models in S extends the S language to fit and question 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 consequences of research in both the computational and statistical aspects of modeling data.
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I am in the administer of learning R, the open source implementation of the S language. This book is one of the classics describing the original S language. Even as some small parts, of this book, are now out of date, it remains a fantastic source, of in rank about the design and use of S and by extension, of R.
Rating: 5 / 5
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 truly dissappointed, unless you just want a large collections of example calls to canned S routines. The authors of the bestow book, on the other hand, are Chambers and Hastie of AT&T (where S was invented), and they clearly know the importance of detailed explanations of the theory underlying the S functions they describe. Just as vital, in my attitude, they also describe the algorithms used by these functions. These two components are gone from other books (like the well loved Venables and Ripley) but they are critical in order to know — and be able to clarify and justify to others — how and why your statistical analyses were performed and what the consequences really mean. The other way of doing statistics (i.e. throwing canned procedures at your data and seeing what pretty graphs and figures you can yield) is meaningless.
Rating: 5 / 5
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 report has it that 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 thoughts discussed in “the green book” (<>, also by John Chambers), this classic S reference is an obligatory 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: Calculated 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
Rating: 5 / 5