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RMstable

Stable Family / Powered Exponential Model


Description

RMstable is a stationary isotropic covariance model belonging to the so called stable family. The corresponding covariance function only depends on the distance r ≥ 0 between two points and is given by

C(r)=e^{-r^α}

where 0 < α ≤ 2.

Usage

RMstable(alpha, var, scale, Aniso, proj)
RMpoweredexp(alpha, var, scale, Aniso, proj)

Arguments

alpha

a numerical value; should be in the interval (0,2] to provide a valid covariance function for a random field of any dimension.

var,scale,Aniso,proj

optional arguments; same meaning for any RMmodel. If not passed, the above covariance function remains unmodified.

Details

The parameter α determines the fractal dimension D of the Gaussian sample paths:

D = d + 1 - α/2

where d is the dimension of the random field. For α < 2 the Gaussian sample paths are not differentiable (cf. Gelfand et al., 2010, p. 25).

Each covariance function of the stable family is a normal scale mixture.

The stable family includes the exponential model (see RMexp) for α = 1 and the Gaussian model (see RMgauss) for α = 2.

The model is called stable, because in the 1-dimensional case the covariance is the characteristic function of a stable random variable (cf. Chiles, J.-P. and Delfiner, P. (1999), p. 90).

Value

RMstable returns an object of class RMmodel.

Author(s)

References

Covariance function

  • Chiles, J.-P. and Delfiner, P. (1999) Geostatistics. Modeling Spatial Uncertainty. New York: Wiley.

  • Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998) Model-based geostatistics (with discussion). Applied Statistics 47, 299–350.

  • Gelfand, A. E., Diggle, P., Fuentes, M. and Guttorp, P. (eds.) (2010) Handbook of Spatial Statistics. Boca Raton: Chapman & Hall/CRL.

Tail correlation function (for 0 < α ≤ 1)

  • Strokorb, K., Ballani, F., and Schlather, M. (2014) Tail correlation functions of max-stable processes: Construction principles, recovery and diversity of some mixing max-stable processes with identical TCF. Extremes, Submitted.

See Also

Examples

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

model <- RMstable(alpha=1.9, scale=0.4)
x <- seq(0, 10, 0.02)
plot(model)
plot(RFsimulate(model, x=x))

RandomFields

Simulation and Analysis of Random Fields

v3.3.10
GPL (>= 3)
Authors
Martin Schlather [aut, cre], Alexander Malinowski [aut], Marco Oesting [aut], Daphne Boecker [aut], Kirstin Strokorb [aut], Sebastian Engelke [aut], Johannes Martini [aut], Felix Ballani [aut], Olga Moreva [aut], Jonas Auel[ctr], Peter Menck [ctr], Sebastian Gross [ctr], Ulrike Ober [ctb], Paulo Ribeiro [ctb], Brian D. Ripley [ctb], Richard Singleton [ctb], Ben Pfaff [ctb], R Core Team [ctb]
Initial release

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