Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Patrick E. Brown
Title: Model-Based Geostatistics the Easy Way
Abstract: This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrates the geostatsp and dieasemapping packages for performing inference using these models. Making use of R’s spatial data types, and raster objects in particular, makes spatial analyses using geostatistical models simple and convenient. Examples using real data are shown for Gaussian spatial data, binomially distributed spatial data, a logGaussian Cox process, and an area-level model for case counts.

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Paper: Model-Based Geostatistics the Easy Way     Download PDF (Downloads: 5591)
Supplements:
geostatsp_1.1.9.tar.gz: R source package Download (Downloads: 419; 1MB)
diseasemapping_1.1.5.tar.gz: R source package Download (Downloads: 357; 465KB)
v63i12.R: R example code from the paper Download (Downloads: 526; 12KB)
DOI: 10.18637/jss.v063.i12

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.