RandomFields (version 3.1.8)

Tail Correlation Funtioncs: Covariance models valid for max-stable random fields

Description

This page summarizes the models that can be use for tail correlation functions

Details

The following models are available Completely monotone function allowing for arbitray scale ll{ RMbcw Model bridging stationary and intrinsically stationary processes for alpha <= 1<="" code=""> and beta < 0 RMdagum Dagum model with $\beta < \gamma$ and $\gamma \le 1$ RMexp exponential model RMgencauchy generalized Cauchy family with $\alpha \le 1$ (and arbitrary $\beta> 0$) RMmatern Whittle-Matern model with $\nu \le 1/2$ RMstable symmetric stable family or powered exponential model with $\alpha \le 1$ RMwhittle Whittle-Matern model, alternative parametrization with $\nu \le 1/2$ } Other isotropic models with arbitray scale ll{ RMnugget nugget effect model } Compactly supported covariance functions ll{ RMaskey Askey's model RMcircular circular model RMconstant identically constant RMcubic cubic model RMgengneiting Wendland-Gneiting model; differentiable models with compact support RMgneiting differentiable model with compact support RMspheric spherical model } Anisotropic models ll{ none up to now. } Basic Operators ll{ RMmult, * product of covariance models RMplus, + sum of covariance models or variograms }

Further Operators ll{ RMbernoulli correlation of binary fields RMbrownresnick tcf of a Brown-Resnick process RMschlather tcf of a Schlather process }

See RMmodels for cartesian models.

References

• Strokorb, K., Ballani, F., and Schlather, M. (2015) Tail correlation functions of max-stable processes: Construction principles, recovery and diversity of some mixing max-stable processes with identical TCF.Extremes,18, 241-271

See Also

coordinate systems, RM, RMmodels, RMtrafo

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
RFgetModelNames(type="tail")

## an example of a simple model
model <- RMexp(var=1.6, scale=0.5) + RMnugget(var=0) #exponential + nugget
plot(model)

FinalizeExample()