| RMnugget {RandomFields} | R Documentation |
RMnugget is a multivariate stationary isotropic covariance model
called “nugget effect”.
The corresponding covariance function only depends on the distance
r ≥ 0 between two points and is given for i,j
in 1,...,vdim by
C_{ij}(r) = δ_ij 1_{0}(r),
where δ_{ij}=1 if i=j and δ_{ij}=0 otherwise.
RMnugget(tol, vdim, var, scale, Aniso, proj)
tol |
Only for advanced users. See |
vdim |
Only for advanced users. See |
var,scale,Aniso,proj |
optional arguments; same meaning for any
|
Note that the argument scale does not affect the covariance
model; Aniso has an effect in case of zonal anisotropy.
The nugget effect belongs to Gaussian white noise and is often used for modeling measurement errors.
The
locations at a distance less than or equal to nugget.tol
are considered as being identical. This strategy applies to
the simulation method and the covariance function itself.
Hence, the covariance function is only positive definite
if nugget.tol=0.0. However, if the anisotropy matrix
does not have full rank and nugget.tol=0.0 then,
the simulations are likely to be odd.
The value of nugget.tol
should be of order 1e-15.
RMnugget returns an object of class RMmodel
Martin Schlather, schlather@math.uni-mannheim.de
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again model <- RMnugget(Aniso=matrix(1, nr=2, nc=2)) x <- seq(0, 10, 0.02) plot(RFsimulate(model, x=x, y=x, tol=1e-10))