Brown-Resnick-Specific: Simulation methods for Brown-Resnick processes

Description

These models define the particular way to simulate Brown-Resnick processes

Usage

RPbrmixed(phi, tcf, xi, mu, s, meshsize, vertnumber, optim_mixed,
          optim_mixed_tol, optim_mixed_maxpo, lambda, areamat, variobound) 
RPbrorig(phi, tcf, xi, mu, s)
RPbrshifted(phi, tcf, xi, mu, s)

Arguments

phi

object of class RMmodel; specifies the covariance model to be simulated.

tcf

the extremal correlation function; either phi or tcf must be given.

xi, mu, s

the shape parameter, the location parameter and the scale parameter, respectively, of the generalized extreme value distribution. See Details.

lambda

positive constant factor in the intensity of the Poisson point processused in the M3 representation, cf. Thm. 6 and Remark 7 in Oesting et. al (2012); can be estimated by setting optim_mixed if unknown. Default value is 1.

areamat

vector or matrix of values in $[0,1]$ with odd length (odd number of rows and columns, respectively). Each value represents the portion of processes whose maximum is located at a specific location on a grid taken into account for the simulation of

meshsize, vertnumber, optim_mixed, optim_mixed_tol, optim_mixed_maxpo, variobound

further arguments for simulation via the mixed moving maxima (M3) representation; see RFoptions

Value

The functions return an object of class RMmodel

Details

The argument xi is always a number, i.e. $\xi$ is constant in space. In contrast, $\mu$ and $s$ might be constant numerical value or given a RMmodel, in particular by a RMtrend model.

The functions RPbrorig, RPbrshifted and RPbrmixed simulate a Brown-Resnick process, which is defined by $$Z(x) = \max_{i=1}^\infty X_i \exp(W_i(x) - \gamma),$$ where the $X_i$ are the points of a Poisson point process on the positive real half-axis with intensity $x^{-2} dx$, $W_i \sim W$ are iid centered Gaussian processes with stationary increments and variogram $\gamma$ given by model. The functions correspond to the following ways of simulation: [object Object],[object Object],[object Object]

References

Oesting, M., Kabluchko, Z. and Schlather M. (2012) Simulation of {B}rown-{R}esnick Processes,Extremes,15, 89-107.

Examples

Run this code

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
  model <- RPbrshifted(RMfbm(alpha=1.5), xi=0)
  z <- RFsimulate(model=model, 0:10, 0:10, n=4)
  plot(z)
FinalizeExample()

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