multivariate quasi-arithmetic mean
RMmqam
is a multivariate stationary covariance model depending
on a submodel phi such that
psi( . ) := phi(sqrt( . ))
is completely monotone, and depending on further stationary
covariance models C_i. The covariance is given by
C_{ij}(h) = φ(√(θ_i (φ^{-1}(C_i(h)))^2 + θ_j (φ^{-1}(C_j(h)))^2 ))
where φ is a completely monotone function, C_i are suitable covariance functions and θ_i≥0 such that ∑_i θ_i=1.
RMmqam(phi, C1, C2, C3, C4, C5, C6, C7, C8, C9, theta, var, scale, Aniso, proj)
phi |
a valid covariance |
C1, C2, C3, C4, C5, C6, C7, C8, C9 |
optional further stationary |
theta |
is a vector of values in [0,1], summing up to 1. |
var,scale,Aniso,proj |
optional arguments; same meaning for any |
Note that psi( . ) :=
phi(sqrt( . )) is completely monotone if and only if
phi is a valid covariance function for all dimensions,
e.g. RMstable
, RMgauss
, RMexponential
.
Warning: RandomFields
cannot check whether the combination
of phi and C_i is valid.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
Porcu, E., Mateu, J. & Christakos, G. (2009) Quasi-arithmetic means of covariance functions with potential applications to space-time data. Journal of Multivariate Analysis, 100, 1830–1844.
RMqam
,
RMmodel
,
RFsimulate
,
RFfit
.
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again RFoptions(modus_operandi="sloppy") model <- RMmqam(phi=RMgauss(),RMgauss(),RMexp(),theta=c(0.4, 0.6), scale=0.5) x <- seq(0, 10, 0.02) plot(model) z <- RFsimulate(model=model, x=x) plot(z) RFoptions(modus_operandi="normal")
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