| Summary: | 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 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, negative binomial GAMM, 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 fishes. 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.
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