Stable Family / Powered Exponential Model
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.
RMstable(alpha, var, scale, Aniso, proj) RMpoweredexp(alpha, var, scale, Aniso, proj)
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
|
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 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).
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
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.
RMbistable
,
RMexp
,
RMgauss
,
RMmodel
,
RFsimulate
,
RFfit
.
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))
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