Bayesian Inference for Multivariate Spatial Models with INLA

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.

Francisco Palmí-Perales (Department of Statistics and Operational Research, Faculty of Mathematics, Universitat de València) , Virgilio Gómez-Rubio (Department of Mathematics, E.T.S. Ingeniería Industrial-Albacete, Universidad de Castilla-La Mancha) , Roger S. Bivand (Department of Economics, Norwegian School of Economics) , Michela Cameletti (Department of Economics, Universitá degli studi di Bergamo) , Håvard Rue (King Abdullah University of Science and Technology)
2023-12-18

0.1 Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-068.zip

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For attribution, please cite this work as

Palmí-Perales, et al., "Bayesian Inference for Multivariate Spatial Models with INLA", The R Journal, 2023

BibTeX citation

@article{RJ-2023-068,
  author = {Palmí-Perales, Francisco and Gómez-Rubio, Virgilio and Bivand, Roger S. and Cameletti, Michela and Rue, Håvard},
  title = {Bayesian Inference for Multivariate Spatial Models with INLA},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-068},
  doi = {10.32614/RJ-2023-068},
  volume = {15},
  issue = {3},
  issn = {2073-4859},
  pages = {172-190}
}

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