Example code with Mapping depth to Pleistocene sand with Bayesian generalised linear geostatistical models

L. Steinbuch; D.J. Brus; G.B.M. Heuvelink

doi:10.5281/zenodo.5005828

Example code with Mapping depth to Pleistocene sand with Bayesian generalised linear geostatistical models

L. Steinbuch^*, D.J. Brus, G.B.M. Heuvelink

^*Corresponding author for this work

Research output: Non-textual form › Software

Abstract

Spatial soil applications frequently involve binomial variables. This code in the R language shows the application of a Bayesian generalised linear model (BGLM; which under certain preconditions equals a generalised linear model) as well as its extension, a Bayesian generalised linear geostatistical model (BGLGM), used on a simulated dataset. The output includes posterior parameters and spatial prediction maps.

Original language	English
Place of Publication	Wageningen
Publisher	Wageningen University & Research
Media of output	Online
DOIs	https://doi.org/10.5281/zenodo.5005828
Publication status	Published - 21 Jun 2021

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10.5281/zenodo.5005828Licence: Open Access other

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title = "Example code with Mapping depth to Pleistocene sand with Bayesian generalised linear geostatistical models",

abstract = "Spatial soil applications frequently involve binomial variables. This code in the R language shows the application of a Bayesian generalised linear model (BGLM; which under certain preconditions equals a generalised linear model) as well as its extension, a Bayesian generalised linear geostatistical model (BGLGM), used on a simulated dataset. The output includes posterior parameters and spatial prediction maps.",

author = "L. Steinbuch and D.J. Brus and G.B.M. Heuvelink",

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N2 - Spatial soil applications frequently involve binomial variables. This code in the R language shows the application of a Bayesian generalised linear model (BGLM; which under certain preconditions equals a generalised linear model) as well as its extension, a Bayesian generalised linear geostatistical model (BGLGM), used on a simulated dataset. The output includes posterior parameters and spatial prediction maps.

AB - Spatial soil applications frequently involve binomial variables. This code in the R language shows the application of a Bayesian generalised linear model (BGLM; which under certain preconditions equals a generalised linear model) as well as its extension, a Bayesian generalised linear geostatistical model (BGLGM), used on a simulated dataset. The output includes posterior parameters and spatial prediction maps.

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M3 - Software

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Example code with Mapping depth to Pleistocene sand with Bayesian generalised linear geostatistical models

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