Book: Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volume II: GAM and Zero-Inflated Models
We are pleased to announce the following book: Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volume II: GAM and Zero-Inflated Models Authors: Zuur and Ieno Book website: www.highstat.com Paperback or EBook can be order (exclusively) from www.highstat.com TOC: http://highstat.com/Books/BGS/SpatialTempVII/TOC_SpatTempII_Online.pdf 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. Outline: 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 this book. 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: highstat at highstat.com URL: www.highstat.com And: 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 Author of: 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). [[alternative HTML version deleted]]