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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.
The book is priced at £39.00. Click to order the book.
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All data sets used in the book are provided as *.txt files. Right-mouse click on a data file and click on Save-As.
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:
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)".