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CRC Press

Generalized Additive Models: An Introduction with R, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

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Description

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self- study. Read more

Publisher ‏ : ‎ Chapman and Hall/CRC; 2nd edition (May 30, 2017)


Language ‏ : ‎ English


Hardcover ‏ : ‎ 476 pages


ISBN-10 ‏ : ‎ 1498728332


ISBN-13 ‏ : ‎ 31


Item Weight ‏ : ‎ 2.31 pounds


Dimensions ‏ : ‎ 7.99 x 10 x 1.85 inches


Best Sellers Rank: #172,482 in Books (See Top 100 in Books) #61 in Statistics (Books) #283 in Probability & Statistics (Books) #6,165 in Unknown


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Top Amazon Reviews

    • Should be the standard statistical methodology for modeling non-linear associations
Generalized additive models are the go-to method for coping with non-linear relations between modeled outcomes and covariates -- this is a topic which should be a standard tool in statistical methodology. I found the 2nd edition of this book much more readable than the 1st. Overall, it provides a clear introduction, theory, and practical examples of generalized additive models. The 2nd edition not only restructures the book, but adds some new material including adaptive smoothing (which strikes me as a good idea when your domain variable is not sampled uniformly) and location-scale modeling (to accommodate heterogeneous variance with a penalized spline of the variance over the domain), and functional data analysis. I think the distributions modeled have also been expanded, with considerable discussion of the available families including exponential and Cox proportional hazards (even a discussion of time-dependent covariates). Oddly, Table 5.1 from the 1st edition has been removed from the 2nd, which I consider a poor choice since the table provided a nice summary of the smoothing bases available, along with their advantages and disadvantages. I attended Simon Wood's short course on GAM at JSM a few years ago in San Diego, and he covered some material there on details of modeling which were not in the 1st edition, but have been added to the current edition -- some other things may not have been included, for instance he discussed issues with concurvity (analog of co-linearity) and spacial correlation with CorGaus, which I didn't notice but may have missed (I skimmed some sections) in this edition. Given the opportunity, I highly recommend his short course on the topic, it is comprehensive and well structured, and Simon is a great teacher. ... show more
Reviewed in the United States on June 29, 2017 by C. Andersen
    • Awesome book for an awesome R package
Great book, great package. GAMs through mgcv have changed my modeling life, and this book is a fantastic manual enabling that... enough theory when I want to dig in deep to a particular facet, readable and including enough examples to help me spin up quickly for practical use, when I don’t need the full technical details, and broad enough to capture my specialized use cases outside of what’s typically presented in online tutorials. Thanks for all of it, Simon Wood! ... show more
Reviewed in the United States on July 23, 2021 by Suz T-W
    • Everything is clear and uncomplicated
The book that teaches how to use these models I really like the approach
Reviewed in the United States on March 24, 2019 by ShimonF
    • R statistics
Good book .
Reviewed in the United States on November 9, 2019 by L.R. Verdooren
    • Not an introduction
I would not recommend this book as an introduction. It is pretty complicated from the beginning. Even though the author tries to be "practical", and gives some useful advice, the theory starts from really high level. To understand even simplest theory, one must know linear algebra perfectly. Also explanations are not too clear, often missing details. Material is illustrated with geometrical plots, which are supposed to be obvious, but are really almost incomprehensible because of lack of detailed explanations. ... show more
Reviewed in the United States on July 14, 2017 by Dimitri K

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