Quasi-arithmetic mean
RMqam
is a univariate 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(h) = phi(sqrt(sum_i theta_i (phi^{-1}(C_i(h)))^2))
RMqam(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 univariate
stationary |
theta |
a vector with positive entries |
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. (2007) Quasi-arithmetic means of covariance functions with potential applications to space-time data. Submitted to Journal of Multivariate Analysis.
RMmqam
,
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 <- RMqam(phi=RMgauss(), RMexp(), RMgauss(), theta=c(0.3, 0.7), scale=0.5) x <- seq(0, 10, 0.02) plot(model) plot(RFsimulate(model, x=x))
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.