Currently, a part of the R statistical software is developed in order to deal with spatial models. More specifically, some available packages allow the user to analyse categorical spatial random patterns. However, only the spMC package considers a viewpoint based on transition probabilities between locations. Through the use of this package it is possible to analyse the spatial variability of data, make inference, predict and simulate the categorical classes in unobserved sites. An example is presented by analysing the well-known Swiss Jura data set.
spMC, gstat, geoRglm, RandomFields
Spatial, SpatioTemporal, Bayesian
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For attribution, please cite this work as
Sartore, "The R Journal: spMC: Modelling Spatial Random Fields with Continuous Lag Markov Chains", The R Journal, 2013
BibTeX citation
@article{RJ-2013-022,
author = {Sartore, Luca},
title = {The R Journal: spMC: Modelling Spatial Random Fields with Continuous Lag Markov Chains},
journal = {The R Journal},
year = {2013},
note = {https://doi.org/10.32614/RJ-2013-022},
doi = {10.32614/RJ-2013-022},
volume = {5},
issue = {2},
issn = {2073-4859},
pages = {16-28}
}