Bayesian Inference for Multivariate Spatial Models with R-INLA

Francisco Palmí-Perales; Virgilio Gómez-Rubio; Roger S Bivand; Michela Cameletti; Haavard Rue

Bayesian Inference for Multivariate Spatial Models with R-INLA

Francisco Palmí-Perales, Virgilio Gómez-Rubio, Roger S Bivand, Michela Cameletti, Haavard Rue

Research output: Contribution to journal › Article › peer-review

Abstract

Bayesian methods and software for spatial data analysis are generally well established now in the scientific community. 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.

Original language	English (US)
Journal	Accepted by The R Journal
State	Published - Sep 6 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-07
Acknowledgements: V. Gómez-Rubio has been supported by grants SBPLY/17/180501/000491 and SBPLY/21/180501/000241, funded by Consejería de Educación, Cultura y Deportes (JCCM, Spain) and Fondo Europeo de Desarrollo Regional, grant MTM2016-77501-P and PID2019-106341GB-I00, funded by Ministerio de Economía y Competitividad (Spain), and grant PID2019-106341GB-I00, funded by Ministerio de Ciencia e Innovación (Spain). F. Palmí-Perales was supported by a doctoral scholarship awarded by the University of Castilla–La Mancha (Spain) and by grant PID2021-128228NB-I00 funded by Ministerio de Ciencia e innovación.

Access to Document

https://repository.kaust.edu.sa/bitstream/10754/686644/5/RJwrapper.pdf

Cite this

@article{9ce2a7f675334819b5c340edafc6f03d,

title = "Bayesian Inference for Multivariate Spatial Models with R-INLA",

abstract = "Bayesian methods and software for spatial data analysis are generally well established now in the scientific community. 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.",

author = "Francisco Palm{\'i}-Perales and Virgilio G{\'o}mez-Rubio and Bivand, {Roger S} and Michela Cameletti and Haavard Rue",

note = "KAUST Repository Item: Exported on 2023-09-07 Acknowledgements: V. G{\'o}mez-Rubio has been supported by grants SBPLY/17/180501/000491 and SBPLY/21/180501/000241, funded by Consejer{\'i}a de Educaci{\'o}n, Cultura y Deportes (JCCM, Spain) and Fondo Europeo de Desarrollo Regional, grant MTM2016-77501-P and PID2019-106341GB-I00, funded by Ministerio de Econom{\'i}a y Competitividad (Spain), and grant PID2019-106341GB-I00, funded by Ministerio de Ciencia e Innovaci{\'o}n (Spain). F. Palm{\'i}-Perales was supported by a doctoral scholarship awarded by the University of Castilla–La Mancha (Spain) and by grant PID2021-128228NB-I00 funded by Ministerio de Ciencia e innovaci{\'o}n.",

year = "2023",

month = sep,

day = "6",

language = "English (US)",

journal = "Accepted by The R Journal",

}

TY - JOUR

T1 - Bayesian Inference for Multivariate Spatial Models with R-INLA

AU - Palmí-Perales, Francisco

AU - Gómez-Rubio, Virgilio

AU - Bivand, Roger S

AU - Cameletti, Michela

AU - Rue, Haavard

N1 - KAUST Repository Item: Exported on 2023-09-07 Acknowledgements: V. Gómez-Rubio has been supported by grants SBPLY/17/180501/000491 and SBPLY/21/180501/000241, funded by Consejería de Educación, Cultura y Deportes (JCCM, Spain) and Fondo Europeo de Desarrollo Regional, grant MTM2016-77501-P and PID2019-106341GB-I00, funded by Ministerio de Economía y Competitividad (Spain), and grant PID2019-106341GB-I00, funded by Ministerio de Ciencia e Innovación (Spain). F. Palmí-Perales was supported by a doctoral scholarship awarded by the University of Castilla–La Mancha (Spain) and by grant PID2021-128228NB-I00 funded by Ministerio de Ciencia e innovación.

PY - 2023/9/6

Y1 - 2023/9/6

N2 - Bayesian methods and software for spatial data analysis are generally well established now in the scientific community. 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.

AB - Bayesian methods and software for spatial data analysis are generally well established now in the scientific community. 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.

UR - http://hdl.handle.net/10754/686644

UR - https://arxiv.org/pdf/2212.10976.pdf

M3 - Article

JO - Accepted by The R Journal

JF - Accepted by The R Journal

ER -

Bayesian Inference for Multivariate Spatial Models with R-INLA

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this