Bayesian Inference for Multivariate Spatial Models with INLA

Department of Statistics and Operational Research, Faculty of Mathematics, Universitat de València

Francisco.Palmi@uv.es

Department of Mathematics, E.T.S. Ingeniería Industrial-Albacete, Universidad de Castilla-La Mancha

Virgilio.Gomez@uclm.es

Department of Economics, Norwegian School of Economics

Roger.Bivand@nhh.no

Department of Economics, Universitá degli studi di Bergamo

michela.cameletti@unibg.it

King Abdullah University of Science and Technology

haavard.rue@kaust.edu.sa

Abstract

Bayesian methods and software for spatial data analysis are well-established now in the broader scientific community generally and in the spatial data analysis community specifically. Despite the wide application of spatial models, the analysis of multivariate spatial data using the integrated nested Laplace approximation through its R package (R-INLA) has not been widely described in the existing literature. Therefore, the main objective of this article is to demonstrate that R-INLA is a convenient toolbox to analyse different types of multivariate spatial datasets. This will be illustrated by analysing three datasets which are publicly available. Furthermore, the details and the R code of these analyses are provided to exemplify how to fit models to multivariate spatial datasets with R-INLA.

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