https://github.com/cran/RandomFields
Tip revision: 34a453721b3918d1a8ce82b414ac3e6435072559 authored by Martin Schlather on 12 September 2016, 08:11:22 UTC
version 3.1.24
version 3.1.24
Tip revision: 34a4537
RPnugget.Rd
\name{Independent Variables}
\alias{Nugget}
\alias{RPnugget}
\title{Method to simulate the Nugget effect}
\description{
Method to simulate the Nugget effect.
}
\usage{
RPnugget(phi, boxcox, tol, vdim)
}
\arguments{
\item{phi}{object of class \code{\link[=RMmodel-class]{RMmodel}};
specifies the covariance model to be simulated. The only possible
model for \code{phi} is \command{\link{RMnugget}}.}
\item{boxcox}{the one or two parameters of the box cox transformation.
If not given, the globally defined parameters are used.
see \command{\link{RFboxcox}} for Details.
}
\item{tol}{
points at a distance less than or equal to \code{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 \code{nugget.tol=0.0}. However, if the anisotropy matrix
does not have full rank and \code{nugget.tol=0.0} then,
the simulations are likely to be odd.
The value of \code{nugget.tol}
should be of order \eqn{10^{-15}}{1e-15}.
Default: \code{0.0}
}
\item{vdim}{positive integer; the model is treated
\code{vdim}-variate, \code{vdim=1} (default) corresponds to a
univariate random field.
Mostly, the value of \code{vdim} is set automatically.
Default is that it takes the value of the submodel \code{phi}}
}
\details{
This method only allows \command{\link{RMnugget}} as a submodel.
The method also allows for zonal nugget effects. Only there the
argument \code{tol} becomes important.
For the zonal nugget effect, the anisotropy matrix \code{Aniso}
should be given in \command{\link{RMnugget}}. There, only the kernal of the
matrix is important.
}
\value{
\code{RPnugget} returns an object of class
\code{\link[=RMmodel-class]{RMmodel}}
}
\references{
\itemize{
\item
Schlather, M. (1999) \emph{An introduction to positive definite
functions and to unconditional simulation of random fields.}
Technical report ST 99-10, Dept. of Maths and Statistics,
Lancaster University.
}
}
\author{Martin Schlather, \email{schlather@math.uni-mannheim.de}
}
\seealso{ \link{Gaussian},
\link{RP},
\command{\link{RPcoins}},
\command{\link{RPhyperplane}},
\command{\link{RPspectral}},
\command{\link{RPtbm}}.
}
\keyword{methods}
\examples{
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
model <- RMnugget()
z <- RFsimulate(model=model, 0:10, 0:10, n=4)
plot(z)
model <- RPnugget(RMnugget(var=0.01, Aniso=matrix(nc=2, rep(1,4))))
z <- RFsimulate(model=model, 0:10, 0:10, n=4)
plot(z)
\dontshow{FinalizeExample()}
}