Journal article
Unified geostatistical modeling for data fusion and spatial heteroskedasticity with R package ramps
Journal of statistical software, Vol.25(10), pp.1-21
2008
DOI: 10.18637/jss.v025.i10
Abstract
This article illustrates usage of the ramps R package, which implements the reparameterized and marginalized posterior sampling (RAMPS) algorithm for complex Bayesian geostatistical models. The RAMPS methodology allows joint modeling of areal and point-source data arising from the same underlying spatial process. A reparametrization of variance parameters facilitates slice sampling based on simplexes, which can be useful in general when multiple variances are present. Prediction at arbitrary points can be made, which is critical in applications where maps are needed. Our implementation takes advantage of sparse matrix operations in the Matrix package and can provide substantial savings in computing time for large datasets. A user-friendly interface, similar to the nlme mixed effects models package, enables users to analyze datasets with little programming effort. Support is provided for numerous spatial and spatiotemporal correlation structures, user-defined correlation structures, and non-spatial random effects. The package features are illustrated via a synthetic dataset of spatially correlated observation distributed across the state of Iowa, USA.
Details
- Title: Subtitle
- Unified geostatistical modeling for data fusion and spatial heteroskedasticity with R package ramps
- Creators
- Brian J SmithJun YanMary Kathryn Cowles
- Resource Type
- Journal article
- Publication Details
- Journal of statistical software, Vol.25(10), pp.1-21
- DOI
- 10.18637/jss.v025.i10
- ISSN
- 1548-7660
- eISSN
- 1548-7660
- Language
- English
- Date published
- 2008
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Holden Comprehensive Cancer Center
- Record Identifier
- 9983985996402771
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