RMnugget: Nugget Effect Covariance Model
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value See Also Examples

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.

1	RMnugget(tol, vdim, var, Aniso, proj)

`tol`	Only for advanced users. See `RPnugget`.
`vdim`	Must be set only for multivariate models (advanced).
`var`	optional argument; same meaning for any `RMmodel`. If not passed, the above covariance function remains unmodified.
`Aniso,proj`	(zonal modelling and repeated measurements(advanced)); see `RPnugget` for details.

The nugget effect belongs to Gaussian white noise and is used for modeling measurement errors or to model spatial ‘nuggets’.

RMnugget returns an object of class RMmodel.

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))