Beginner's Guide to Generalized Additive Models with R. Zuur, AF.

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Table of contents

Data sets used in the book

 

Outline

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

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• Table of Contents
• Preface

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Copyright statement

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Data sets used in the book

All data sets used in the book are provided as *.txt files. Right-mouse click on a data file and click on Save-As.

• HighstatLibV4.R support file
• Chapter 1: Human visual system data
• Chapter 2: Bailey fisheries data
• Chapter 3: Pelagic bioluminescent organisms
• Chapter 4: Bailey fisheries data
• Chapter 5: Squid data
• Chapter 6: Gannets data
• Chapter 7: Parasites in hake data