https://github.com/cran/RandomFields
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Tip revision: fab3d29ef16569604858ee648b9e1f6f7d4a7c96 authored by Martin Schlather on 21 September 2014, 00:00:00 UTC
version 3.0.42
Tip revision: fab3d29
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
model <- RMcurlfree(RMgauss(), scale=4)
plot(model, dim=2)

x.seq <- y.seq <- seq(-10, 10, if (interactive()) 0.2 else 5)
simulated <- RFsimulate(model=model, x=x.seq, y=y.seq)
plot(simulated, select.variables=list(1, c(1, 2:3), 4))
\dontshow{FinalizeExample()}
}
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