Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-04-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4565 |
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| author | Matthew Sainsbury-Dale Andrew Zammit-Mangion Noel Cressie |
| author_facet | Matthew Sainsbury-Dale Andrew Zammit-Mangion Noel Cressie |
| author_sort | Matthew Sainsbury-Dale |
| collection | DOAJ |
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Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. In this paper, we describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a generalized linear mixed model framework. These vastly more general spatial and spatio-temporal models are fitted using the Laplace approximation via the software TMB. The existing functionality of FRK is retained with this advance into non-Gaussian models; in particular, it allows for automatic basis-function construction, it can handle both point-referenced and areal data simultaneously, and it can predict process values at any spatial support from these data. This new version of FRK also allows for the use of a large number of basis functions when modeling the spatial process, and thus it is often able to achieve more accurate predictions than previous versions of the package in a Gaussian setting. We demonstrate innovative features in this new version of FRK, highlight its ease of use, and compare it to alternative packages using both simulated and real data sets.
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| format | Article |
| id | doaj-art-e86f7b87d3fb430784dbe707d19af8f1 |
| institution | DOAJ |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-e86f7b87d3fb430784dbe707d19af8f12025-08-20T02:51:00ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-04-01108110.18637/jss.v108.i10Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRKMatthew Sainsbury-Dale0Andrew Zammit-Mangion1Noel Cressie2University of WollongongUniversity of WollongongUniversity of Wollongong Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. In this paper, we describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a generalized linear mixed model framework. These vastly more general spatial and spatio-temporal models are fitted using the Laplace approximation via the software TMB. The existing functionality of FRK is retained with this advance into non-Gaussian models; in particular, it allows for automatic basis-function construction, it can handle both point-referenced and areal data simultaneously, and it can predict process values at any spatial support from these data. This new version of FRK also allows for the use of a large number of basis functions when modeling the spatial process, and thus it is often able to achieve more accurate predictions than previous versions of the package in a Gaussian setting. We demonstrate innovative features in this new version of FRK, highlight its ease of use, and compare it to alternative packages using both simulated and real data sets. https://www.jstatsoft.org/index.php/jss/article/view/4565 |
| spellingShingle | Matthew Sainsbury-Dale Andrew Zammit-Mangion Noel Cressie Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK Journal of Statistical Software |
| title | Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK |
| title_full | Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK |
| title_fullStr | Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK |
| title_full_unstemmed | Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK |
| title_short | Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK |
| title_sort | modeling big heterogeneous non gaussian spatial and spatio temporal data using frk |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4565 |
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