RandomFields {RandomFields} | R Documentation |
The package RandomFields
allows for simulating various kinds
of random fields. The emphasis is (still) on unconditional simulation
of stationary and isotropic
Gaussian random fields. Furthermore, algorithms for
conditional simulation and simulation of
max-stable random fields are provided.
Additionally, the package includes tools for analysing spatial data: empirical variogram, interactive fitting, and MLE estimation of parameters. Basic kriging procedures are also provided.
Version 2.0 will contain various extensions of the packages (anisotropic random fields, user defined nested models and multiplicative models). It is is supposed to be released the latest by the end of 2002.
The following random fields and related functionallities are provided by the package.
CondSimu
: conditional simulation
CovarianceFct
: covariance functions or
variogram models
EmpiricalVariogram
: empirical variogram
GaussRF
: simulation of Gaussian random
fields; nice examples to get familiar with the
simulation features of the package
Kriging
: simple and ordinary kriging
mleRF
: maximum likelihood estimator for
random field parameters
RFparameters
: control parameters (advanced settings)
PrintMethodList
: list of implemented
simulation methods
ShowModels
: interactive, graphical choice of
models
soil
: Soil physical and chemical data;
the example
gives a simple geostatistical analysis using
features of the package
CovarianceFct
: covariance models for
extremal Gaussian random fields
MaxStableRF
: simulation of max-stable
random fields
RFparameters
: control parameters (advanced)
Functions used for diverse simulation methods:
DeleteRegister
: deleting internal registers
Many thanks to Martin Maechler, Paulo Ribeiro, and Tilmann Gneiting for proof-reading parts of the code and the help text of this package.
The work has been supported by the EU TMR network ERB-FMRX-CT96-0095 on ``Computational and statistical methods for the analysis of spatial data'' in 1999, and by the German Federal Ministry of Research and Technology (BMFT) grant PT BEO 51-0339476C during 20002001.
Martin Schlather, Martin.Schlather@uni-bayreuth.de http://www.geo.uni-bayreuth.de/~martin
Schlather, M. (2001) Simulation of stationary and isotropic random fields. R News 1 (2), 18-20.
Schlather, M. (1999) An introduction to positive definite functions and to unconditional simulation of random fields. Technical report ST 99-10, Dept. of Maths and Statistics, Lancaster University.