Statistical Models in S Review

Average Reviews:

(More customer reviews)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

Click Here to see more reviews about: Statistical Models in S

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linearmodels, 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.

Get 23% OFF

Buy cheap Statistical Models in S now.