Bayesian spatially varying coefficient models in the spBayes R package. (March 2020)

• Record Type: Journal Article
• Authors: Finley, Andrew O.; Banerjee, Sudipto
• Is Part Of: Environmental modelling & software. Volume 125(2020)
• Publication Date: 2020-03
• Subjects: MCMC -- Multivariate Gaussian process -- Kriging -- R; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104608
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper describes and illustrates new functionality for fitting spatially varying coefficients models in the spBayes (version 0.4–2) R package. The new spSVC function uses a computationally efficient Markov chain Monte Carlo algorithm and extends current spBayes functions, that fit only space-varying intercept regression models, to fit independent or multivariate Gaussian process random effects for any set of columns in the regression design matrix. Newly added OpenMP parallelization options for spSVC are discussed and illustrated, as well as helper functions for joint and point-wise prediction and model fit diagnostics. The utility of the proposed models is illustrated using a PM10 analysis over central Europe.