Generalized Additive Models: An Introduction with R

by Simon Wood

Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models.   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. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

410 pages, Hardcover

First published February 27, 2006

Reviews

[Anna Nesterovich · Rating 2 out of 5]

This book starts with an excellent sentence: "I assume that most people are interested in statistical models in order to use them, rather than to gaze upon the mathematical beauty of their structure, and for this reason I have tried to keep this book practically focused" (Preface, XVI). I was full of hope! Here, finally, a statistician, who can explain how and why to use statistics (this particular model, in this case). Well, the greater the hope, the greater the disappointment. This book neither helped me to understand how the GAM works and why should I use it, nor explained what do I need to do to make it work in R. The examples given in the book are all start with "simulate some data", which makes it impossible to apply to real data. At least for me. I'm not a statistician, but I have to use statistics in my research. That's why there are so many statistics text out there, to help people like me, right? Nope. They usually make the process only harder. This book is no worse than other books on the subject, but this fact doesn't make this one any better.