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A Beginner's Guide to Generalized Additive Models with R is, as the title implies, a practical handbook for the non-statistician. The author's philosophy is that the shortest path to comprehension of a statistical technique without delving into extensive mathematical detail is through programming its basic principles in, for example, R. Not a series of cookbook exercises, the author uses data from biological studies to go beyond theory and immerse the reader in real-world analysis with its inherent untidiness and challenges. The book begins with a review of multiple linear regression using research on human crania size and ambient light levels and continues with an introduction to additive models based on deep sea fishery data. Research on pelagic bioluminescent organisms demonstrates simple linear regression techniques to program a smoother. In Chapter 4 the deep sea fishery study is revisited for a discussion of generalized additive models. The remaining chapters present detailed case studies illustrating the application of Gaussian, Poisson, negative binomial, zero-inflated Poisson, and binomial generalized additive models using seabird, squid, and fish parasite studies.
1 Review of multiple linear regression
2 Introduction to additive models using deep-sea fisheries data
3 Technical aspects of GAM using pelagic bioluminescent organisms
4 Introducing generalized additive models using deep-sea fishery data
5 Additive modelling applied on stable isotope ratios of ocean squid
6 Generalized Additive Models applied on northern gannets
7 Generalized Additive Models applied on parasites of Argentine hake
Alain F. Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has taught statistics to more than 5000 ecologists.
