We are pleased to announce the
following book:
Spatial, Temporal and Spatial-Temporal Ecological Data Analysis
with R-INLA
Authors: Zuur, Ieno, Saveliev
Book website:
www.highstat.com
Paperback or EBook can be order (exclusively) from
www.highstat.com
TOC:
http://highstat.com/Books/BGS/SpatialTemp/Zuuretal2017_TOCOnline.pdf
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.
Outline
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.
--
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).
Highland Statistics Ltd.
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UK - AB41 6DZ Newburgh
Tel: 0044 1358 788177
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URL:
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