New book: Beginner’s Guide to GAMM with R
It has taken a while but our ‘Beginner’s Guide to Generalized Additive Mixed Models with R‘ is finally available as paperback and EBook from www.highstat.com. The book is co-authored with Prof Saveliev and Dr Ieno. The hardcover will be available from June 2014 onwards.
This book begins with an introduction to generalised additive models (GAM) using stable isotope ratios from squid. In Chapter 2 we explain additive mixed effects models using polar bear movement data. In Chapter 3 we apply additive mixed effects models on coral reef data.
Ruddy turnstone data are used in Chapter 4 to explain Poisson generalised additive mixed effects models (GAMMs) using the gamm4 package. A simulation study is applied to investigate the effect unbalanced random effects. In Chapter 5 parasite data sampled on anchovy fishes are used to explain overdispersed Poisson GAMM, physician negative binomial GAMM, patient and NB-P GAMM models. We briefly discuss generalised Poisson models for underdispersed data.
In Chapters 6 and 7 two-dimensional smoothers are applied on zero-inflated guillemots and harbour porpoise datasets. A short revision of zero-inflated models is included. Gamma GAMMs are applied on two-way nested tree data in Chapter 8. In Chapter 9 binary nested data are analysed using binomial GAMM.
In Chapter 10 we analyse maximum length of cod fish. The generalised extreme value distribution is used. The data are from a large number of spatial locations and we use INLA to implement spatial correlation. In Chapter 11 sea ducks are analysed using zero-inflated Poisson GAMMs (and GLMMs) with spatial correlation. We again use INLA.
Throughout the book we contrast frequentist and Bayesian approaches. All R code is either included and explained in the book or is available from the website for the book.
I realize that the book is not cheap and I apologize for that. However, it took 1 year to write the book, it contains specialized knowledge and the market for it is relatively small.