Statistical Models in S


Manufacturer: Chapman & Hall/CRC
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Authors:
  • J. M. Chambers
  • T.J. Hastie

Description:



Statistical Models in S
Reviews:

starsSimply the Best (for those who want to know what they're doing)
If you liked to really know, what you buy to do, if you use S, you this book. Do not waste your money on a book such as Venables and Ripley -- you are wund dissappointed, it are you straight large accumulations of example calls to preserved S programs wish. The authors of the present book are on the other hand areas and Hastie from ON


starsThis is "the white book", an essential S-PLUS reference.
S programmers refer to this as "the white book", and it is a key reference for understanding the methods, who in several high end to statistic functions S-PLUSES ', ' lm() ', predict() ', ' design() ', ' aov() ', ' glm() ', ' gam() ', ' loess() ', ' tree() ', ' burl.tree() ', ' nls() ' and ' ms() ' are inclusively introduced. It is apparent from pressure, but it should not be. Even with the new arrival of the S-PLUSES releases, S version 4 contained and many of the ideas discussed "in the green book" (, also by John Chambers), is these classical S reference an indispensable tool for the serious statistician. It must be published -- with a white cover again, naturally. Here the titles the chapters are, as reference: 1. An aperitif 2. Statistic models 3. Data for models 4. Linear models 5. Analysis of variance: Sketched experiments 6. generalized linear models 7. Generalized auxiliary models 8. Local one of recovery models 9. Tree-created models 10. Nonlinear models A. Kategorien and Methoden: Object-oriented programming in S B. S functions and categories reference index



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