Beginner's Guide to Generalized Additive Models with R (2012)

Zuur, AF

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.


Table of contents and Preface

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Data sets and R code used in the book

All data sets used in the book are provided as *.txt files. Right-mouse click on a data file and R code file and click on Save-As. Note that the R code may be intimidating without the text in the book.

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

HighstatLibV8.R support file (replaces earlier HighstatLib versions)

Newer mgcv and R versions may give slightly different results. The R code is fully explained in the book.

Current Errata list for the book


Support material

Readers of the some of our books have free access to Chapter 1 of Beginner's Guide to Generalized Additive Models with R (2012). Zuur AF. This chapter provides an introduction to multiple linear regression, which is prerequisite knowledge for Beginner's Guide to GLM and GLMM with R.

This chapter is password protected. The password is given in the preface of the book that you bought.


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