Analysis, Simulation and Prediction of Multivariate Random Fields with Package<b>RandomFields</b>

Schlather, Martin; Malinowski, Alexander; Menck, Peter J.; Oesting, Marco; Strokorb, Kirstin

doi:10.18637/jss.v063.i08

Cited by 191 publications

(153 citation statements)

References 41 publications

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“…An alternative way of choosing a spatial covariance function is given through the covmodel function, which calls CovarianceFct from the package RandomFields (Schlather, Malinowski, Menck, Oesting, and Strokorb 2015), e.g., a Matérn covariance function with roughness parameter 1, can be obtained with covmodel("matern", pars = 1) in place of ExponentialCovFct(). Note that for covariance functions defined using the function covmodel, any additional parameters (in this case the roughness parameter) are not estimated, rather they are fixed (in this case at 1).…”

Section: Simulating Datamentioning

confidence: 99%

spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

Taylor¹,

Rowlingson²

2017

J. Stat. Soft.

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Survival methods are used for the statistical modelling of time-to-event data. Survival data are characterized by a set of complete records, in which the time of the event is known; and a set of censored records, in which the event was known to have occurred in an interval. When survival data are spatially referenced, the spatial variation in survival times may be of scientific interest. In this article, we introduce a new R package, spatsurv, for inference with spatially referenced survival data. The specific type of model fitted by this package is a parametric proportional hazards model in which the spatially correlated frailties are modelled by a log-Gaussian stochastic process. The package is extensible in that it allows the user to easily create new models for the baseline hazard function and spatial covariance function. The package implements an advanced adaptive Markov chain Monte Carlo algorithm to deliver Bayesian inference with minimal input from the user.A particular feature of the new package is the ability to handle large datasets via the use of auxiliary frailties on a regular grid and the technique of circulant embedding for fast matrix computations. We demonstrate the new package on a real-life dataset.

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Section: Simulating Datamentioning

confidence: 99%

spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

Taylor¹,

Rowlingson²

2017

J. Stat. Soft.

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“…Following the model's hierachical description in Section 2, we simulated forward as follows: In Step 1, we simulated a Gaussian random field ε on the domain D with spatial covariance Σ, using the the R package RandomFields ( [43]). In Step 2, we simulated independent Bernoulli random variables.…”

Section: Parameter Settingsmentioning

confidence: 99%

Two-scale spatial models for binary data

Hardouin¹,

Cressie²

2017

Stat Methods Appl

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A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the back-ground. The background behaviour is captured through a hidden Gaussian process after a logit transformation on its Bernoulli success probabilities. The likelihood is then the product of the (conditional) autologistic probability distribution and the hidden Gaussian-Bernoulli process. The parameters of the new model come from both spatial scales. A series of simulations illustrates the spatial-dependence properties of the model and likelihood-based methods are used to estimate its parameters. Presence-absence data of corn borers in the roots of corn plants are used to illustrate how the model is fitted. Abstract A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background. The background behaviour is captured through a hidden Gaussian process after a logit transformation on its Bernoulli success probabilities. The likelihood is then the product of the (conditional) autologistic probability distribution and the hidden Gaussian-Bernoulli process. The parameters of the new model come from both spatial scales. A series of simulations illustrates the spatial-dependence properties of the model and likelihood-based methods are used to estimate its parameters. Presence-absence data of corn borers in the roots of corn plants are used to illustrate how the model is fitted.

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“…Schlather, Malinowski, Menck, Oesting, and Strokorb (2015) describe recent developments in RandomFields that concern the modeling of multivariate random fields. Padoan and Bevilacqua (2015) analyze random fields by composite likelihood methods, an approach motivated by large data sets.…”

Section: Geostatisticsmentioning

confidence: 99%

Software for Spatial Statistics

Pebesma¹,

Bivand²,

Ribeiro³

2015

J. Stat. Soft.

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We give an overview of the papers published in this special issue on spatial statistics, of the Journal of Statistical Software. 21 papers address issues covering visualization (micromaps, links to Google Maps or Google Earth), point pattern analysis, geostatistics, analysis of areal aggregated or lattice data, spatio-temporal statistics, Bayesian spatial statistics, and Laplace approximations. We also point to earlier publications in this journal on the same topic.

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Analysis, Simulation and Prediction of Multivariate Random Fields with PackageRandomFields

Cited by 191 publications

References 41 publications

spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

Two-scale spatial models for binary data

Software for Spatial Statistics

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