Summary: In Volume II we apply zero-inflated models and generalised
additive (mixed-effects) models to spatial and spatial-temporal data.
Data and all R code is available.
In Chapter 18 we will explain how to deal with zero-inflated data. We
introduce so-called zero-inflated Poisson (ZIP) models, zero-inflated
negative binomial (ZINB) models, zero-altered Poisson (ZAP) models and
zero-altered negative binomial (ZINB) models.
In Chapter 19 we extend the ZIP, ZINB, ZAP and ZANB models with spatial
correlation. Both these chapters use a skate data set from South
America. In the appendix accompanying Chapter 19 we also explain how to
manipulate maps and create spatial polygons (e.g. for coastlines).
In Chapter 20 we revisit a data set with which we have been battling
since 2006. It is about begging behaviour of owl nestlings. In Zuur
(2009a) we applied linear mixed-effects models on it, and in Zuur et al.
(2012a) we analysed it with a zero-inflated GLMM. Thanks to R-INLA we
finally cracked this data set and apply a zero-inflated GAMM.
In Chapter 21 we analyse sandeel count data. This work came out of a
consultancy project that we carried out for Wageningen Marine Research
(The Netherlands) in 2017. Although the setup of the experiment is
simple (approximately 400 sites sampled once per year, for 4 years),
analysing these data and writing this chapter took about 30 days. This
should give you an idea about the complexity of the statistical tools
(zero-inflated GAMMs + spatial-temporal correlation) that we discuss in
Chapter 22 is about zero-inflated bird densities sampled in the Labrador
Sea, located between the Labrador Peninsula (Eastern Canada) and
Greenland. This chapter is about the analysis of zero-inflated
continuous data with spatial correlation. A zero-altered gamma model
with spatial correlation is used.
In Chapter 23 we analyse coral reef data sampled around an island. A lot
of misery comes together in this chapter: smoothers, zero-inflation and
spatial dependency that should not cross land as benthic species that
live in a coral reef do not walk over land! We will discuss barrier
models (Bakka et al. 2018) which ensure that spatial correlation seeps
around a barrier (in this case an island).
Up to Chapter 23 all data sets analysed were geostatistical data and not
areal or lattice data. The reason for this is that most ecological data
is geostatistical. In Chapter 24 we analyse aggregated tornado data in
102 counties in Illinois. This is areal data. We will use various CAR
models (e.g. iCAR, BYM, BYM2) for zero-inflated spatial and
spatial-temporal correlated data.
Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: [hidden email] URL: www.highstat.com
NIOZ Royal Netherlands Institute for Sea Research,
Department of Coastal Systems, and Utrecht University,
P.O. Box 59, 1790 AB Den Burg,
Texel, The Netherlands
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).