| GaussianFields {RandomFields} | R Documentation |
Here, all the methods (models) for simulating Gaussian random fields are listed
RPcirculant | simulation by circulant embedding |
RPcutoff | simulation by a variant of circulant embedding |
RPcoins | simulation by random coin / shot noise |
RPgauss | generic model that chooses automatically among the specific methods |
RPhyperplane | simulation by hyperplane tessellation |
RPintrinsic | simulation by a variant of circulant embedding |
RPnugget | simulation of (anisotropic) nugget effects |
RPsequential | sequential method |
RPspecific | model specific methods (very advanced) |
RPspectral | spectral method |
RPtbm | turning bands |
Martin Schlather, schlather@math.uni-mannheim.de http://ms.math.uni-mannheim.de/de/publications/software
Chiles, J.-P. and Delfiner, P. (1999) Geostatistics. Modeling Spatial Uncertainty. New York: Wiley.
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.
Schlather, M. (2010) On some covariance models based on normal scale mixtures. Bernoulli, 16, 780-797.
Schlather, M. (2011) Construction of covariance functions and unconditional simulation of random fields. In Porcu, E., Montero, J.M. and Schlather, M., Space-Time Processes and Challenges Related to Environmental Problems. New York: Springer.
Yaglom, A.M. (1987) Correlation Theory of Stationary and Related Random Functions I, Basic Results. New York: Springer.
Wackernagel, H. (2003) Multivariate Geostatistics. Berlin: Springer, 3nd edition.
RP,
Other models,
RMmodel,
RFsimulateAdvanced
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again x <- seq(0, 10, 0.01) z <- RFsimulate(RMexp(), x) RFgetModelInfo(RFsimulate, level=0, which="internal") # i.e., circulant embedding has been chosen