Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volume I: Using GLM and GLMM. (2017). Zuur AF, Ieno EN, Saveliev AA 

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This book, Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA, consists of two volumes. You are viewing the website for Volume I, Using GLM and GLMM. Volume II is entitled Using GAM and Zero-Inflated Models, and will be available mid 2017.

Table of Contents

Pdf of Chapter 1

Volume I

In Volume I we explain how to apply linear regression models, generalised linear models (GLM), and generalised linear mixed-effects models (GLMM) to spatial, temporal, and spatial-temporal data. The models that will be employed use the Gaussian and gamma distributions for continuous data, the Poisson and negative binomial distributions for count data, the Bernoulli distribution for absence–presence data, and the binomial distribution for proportional data.

Volume II

In Volume II we apply zero-inflated models and generalised additive (mixed-effects) models to spatial and spatial-temporal data. We also discuss models with more exotic distributions like the generalised Poisson distribution to deal with underdispersion and the beta distribution to analyse proportional data.

Outline of Volume I
In Chapter 2 we discuss an important topic: dependency. Ignoring this means that we have pseudoreplication. We present a series of examples and discuss how dependency can manifest itself.
We briefly discuss frequentist tools that are available for the analysis of temporal and spatial data in Chapters 3 and 4, and we will conclude that their application is rather limited, especially if non-Gaussian distributions are required. We will therefore consider alternative models, but these require Bayesian techniques.

In Chapter 5 we discuss linear mixed-effects models to analyse hierarchical (i.e. clustered or nested) data, and in Chapter 6 we outline how we add spatial and spatial-temporal dependency to regression models via spatial (and/or temporal) correlated random effects.
In Chapter 7 we introduce Bayesian analysis, Markov chain Monte Carlo techniques (MCMC), and Integrated Nested Laplace Approximation (INLA). INLA allows us to apply models to spatial, temporal, or spatial-temporal data.

In Chapters 8 through 16 we present a series of INLA examples. We start by applying linear regression and mixed-effects models in INLA (Chapters 8 and 9), followed by GLM examples in Chapter 10. In Chapters 11 through 13 we show how to apply GLM models on spatial data. In Chapter 14 we discuss time-series techniques and how to implement them in INLA. Finally, in Chapters 15 and 16 we analyse spatial-temporal models in INLA.

Data and R code

The data and all the R code used in the book will be available from March 2017 onwards.