Summary: 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.
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
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.
Dr. Alain F. Zuur
First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).
1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).