| RMvector {RandomFields} | R Documentation |
RMvector is a multivariate covariance model which depends on
a univariate covariance model that is stationary in the first Dspace
coordinates h and where the covariance function phi(h,t)
is twice differentiable in the first component h.
The corresponding matrix-valued covariance function C of the model
only depends on the difference h between two points in the
first component.
It is given by
C(h,t)=( -0.5 * (a + 1) \Delta + a \nabla \nabla^T ) C_0(h, t)
where the operator is applied to the first component h only.
RMvector(phi, a, Dspace, var, scale, Aniso, proj)
phi |
an |
a |
a numerical value; should be in the interval |
Dspace |
an integer; either 2 or 3; the first |
var, scale, Aniso, proj |
optional arguments; same meaning for any
|
C_0 is either a spatio-temporal model (then t is the
time component) or it is an isotropic model. Then, the first Dspace
coordinates are considered as h coordinates and the remaining
ones as t coordinates. By default, Dspace equals the
dimension of the field (and there is no t component).
If a=-1 then the field is curl free; if a=1 then the field is
divergence free.
RMvector returns an object of class RMmodel.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
Scheuerer, M. and Schlather, M. (2012) Covariance Models for Divergence-Free and Curl-Free Random Vector Fields. Stochastic Models 28:3.
RMcurlfree,
RMdivfree,
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 <- RMvector(RMgauss(), scale=0.3)
x <- seq(0, 10, 0.4)
plot(RFsimulate(model, x=x, y=x, z=0), select.variables=list(1:2))