Chambers J.M., Hastie T.J. Statistical Models in S

Chapman and Hall/CRC, 1997. – 620 p. – ISBN: 0412053012, 0412052911, 9780412053016Statistical 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.
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:
An Appetizer
Statistical Models
Data for Models
Linear Models
Analysis of Variance: Designed Experiments
Generalized Linear Models
Generalized Additive Models
Local Regression Models
Tree-Based Models
Nonlinear Models
A. Classes and Methods: Object-oriented Programming in S
B. S Functions and Classes