https://github.com/cran/RandomFields
Tip revision: 919f138ae97c73da2321579cf0a01351bf9ebff3 authored by Martin Schlather on 11 October 2016, 18:32:27 UTC
version 3.1.24.1
version 3.1.24.1
Tip revision: 919f138
RMcurlfree.Rd
\name{RMcurlfree}
\alias{RMcurlfree}
\title{Curlfree Covariance Model}
\description{
\command{\link{RMcurlfree}}
is a multivariate covariance model which depends on
a univariate stationary covariance model where the covariance function phi(h)
is twice differentiable.
The corresponding matrix-valued covariance function C of the model
only depends on the difference \eqn{h}{h} between two points and it is
given by
\deqn{
C(h)=( - \nabla_h (\nabla_h)^T ) C_0(h)
}
}
\usage{
RMcurlfree(phi, var, scale, Aniso, proj)
}
\arguments{
\item{phi}{a univariate stationary covariance model (2 or 3 dimensional).}
\item{var,scale,Aniso,proj}{optional arguments; same meaning for any
\command{\link{RMmodel}}. If not passed, the above
covariance function remains unmodified.}
}
\details{
The model returns the potential field in the first component, the
corresponding curlfree field and field of sources and sinks in the
last component.
See also the models \command{\link{RMdivfree}} and \command{\link{RMvector}}.
}
\value{
\command{\link{RMcurlfree}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}
}
\references{
\itemize{
\item Scheuerer, M. and Schlather, M. (2012)
Covariance Models for Divergence-Free and Curl-Free Random Vector Fields.
\emph{Stochastic Models} \bold{28:3}.
}
}
\author{Martin Schlather, \email{schlather@math.uni-mannheim.de}
}
\seealso{
\command{\link{RMdivfree}},
\command{\link{RMvector}},
\command{\link{RMmodel}},
\command{\link{RFsimulate}},
\command{\link{RFfit}}.
}
\keyword{spatial}
\keyword{models}
\examples{
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
\dontshow{StartExample()}
model <- RMcurlfree(RMgauss(), scale=4)
plot(model, dim=2)
x.seq <- y.seq <- seq(-10, 10, 0.2)
simulated <- RFsimulate(model=model, x=x.seq, y=y.seq)
plot(simulated, select.variables=list(1, c(1, 2:3), 4))
\dontshow{FinalizeExample()}
}