spTimer: Spatio-temporal Bayesian modelling using R
spTimer: Spatio-temporal Bayesian modelling using R
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time.
This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning
of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.
1-32
Bakar, Khandoker Shuvo
007f9be7-6423-48e6-a0e3-de9f740e927c
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf
16 February 2015
Bakar, Khandoker Shuvo
007f9be7-6423-48e6-a0e3-de9f740e927c
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf
Bakar, Khandoker Shuvo and Sahu, Sujit
(2015)
spTimer: Spatio-temporal Bayesian modelling using R.
Journal of Statistical Software, 63 (15), .
(doi:10.18637/jss.v063.i15).
Abstract
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time.
This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning
of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.
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e-pub ahead of print date: 16 February 2015
Published date: 16 February 2015
Identifiers
Local EPrints ID: 412405
URI: http://eprints.soton.ac.uk/id/eprint/412405
PURE UUID: cca6ad24-2fb3-4dbd-a5c1-f1a2d8df9b39
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Date deposited: 17 Jul 2017 13:38
Last modified: 16 Mar 2024 03:15
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Author:
Khandoker Shuvo Bakar
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