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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.

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This book is copyright material from Highland Statistics Ltd. Scanning
this book (or parts of it) and distributing the digital media
(including uploading to the Internet) without our explicit permission is
copyright infringement. Infringing copyright is a criminal offence and you
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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.

Chapter |
Data sets |
R code |
Remarks, extra flowcharts, etc. |

1 | Human visual system data | Chapter1.R |
Different
smoothers graph (updated) R code of this chapter is intimidating without the text in the chapter |

2 | Bailey fisheries data | Chapter2.R | R code of this chapter is intimidating without the text in the chapter |

3 | Pelagic bioluminescent organisms | Chapter3.R | R code of this chapter is intimidating without the text in the chapter |

4 | Bailey fisheries data | Chapter4.R | Corrected Figure 4.5 |

5 | Squid data | Chapter5.R | |

6 | Gannets data | Chapter6.R | |

7 | Parasites in hake data | Chapter7.R | |

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 |

Readers of the book '*Beginner's Guide to GLM and GLMM with R*'
(2013) by Zuur, Hilbe and Ieno 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*. Readers of *Beginner's
Guide to GLM and GLMM with R* also have free access to Chapter 1 of *Zero Inflated Models and Generalized Linear Mixed
Models with R* (2012). Zuur AF, Saveliev AA, Ieno EN. These two chapters are downloadable
from:

- Introduction to Bayesian
statistics, Markov Chain Monte Carlo techniques, and WinBUGS.
Chapter 1 in:
*Zero Inflated Models and Generalized Linear Mixed Models with R*(2012). Zuur AF, Saveliev AA, Ieno EN. We are in the process of changing the WinBUGS code in this chapter to JAGS code. The modified chapter with JAGS code will be available in July 2013. - Review of multiple linear
regression. Chapter 1 in:
*A Beginner’s Guide to Generalized Additive Models with R*(2012). Zuur AF.

Both chapters are password protected. The password is given on page vi in
the preface of *Beginner's Guide to GLM and GLMM with R*. See the paragraph labelled "Chapter 1 of Zuur et al. (2012a)
and Zuur (2012b)".