Statistical Models in S

Rating:
Summary: This is "the white book", an essential S-PLUS reference.
Review: 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" (<>, 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