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Generalized additive models : an introduction with R

Author: Simon N Wood
Publisher: Boca Raton, FL : Chapman & Hall/CRC, 2006.
Series: Texts in statistical science.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Wood, Simon.
Generalized Additive Models : An Introduction with R.
Boca Raton, FL : Chapman & Hall/CRC, ©2006
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Simon N Wood
ISBN: 9781420010404 1420010409
OCLC Number: 908079042
Description: 1 online resource (404 pages).
Contents: Linear models --
Generalized linear models --
Introducing GAM's --
Some GAM theory --
GAMs in practice: mgcv --
Mixed models and GAMMs.
Series Title: Texts in statistical science.
Responsibility: Simon N. Wood.

Abstract:

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv.

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