Nugget Effect Covariance Model
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, Aniso, proj)
tol |
Only for advanced users. See |
vdim |
Must be set only for multivariate models (advanced). |
var |
optional argument; same meaning for any
|
Aniso,proj |
(zonal modelling and repeated
measurements(advanced)); see |
The nugget effect belongs to Gaussian white noise and is used for modeling measurement errors or to model spatial ‘nuggets’.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
RMmodel,
RFsimulate,
RFfit,
RPnugget (advanced users).
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again x <- y <- 1:2 xy <- as.matrix(expand.grid(x, y)) ## we get 4 locations ## Standard use of the nugget effect model <- RMnugget(var = 100) RFcovmatrix(model, x=xy) as.vector(RFsimulate(model, x=x, y=x, tol=1e-10))
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