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\name{RFcovmatrix}
\alias{RFcovmatrix}
\title{Covariance matrix}
\description{
\command{\link{RFcovmatrix}} returns the covariance matrix for a set of points;
}
\usage{
RFcovmatrix(model, x, y = NULL, z = NULL, T = NULL, grid, params,
distances, dim,...)
}
\arguments{
\item{model,params}{\argModel }
\item{x}{\argX}
\item{y,z}{\argYz}
\item{T}{\argT}
\item{grid}{\argGrid}
\item{distances,dim}{\argDistances}
\item{...}{\argDots}
}
\details{
\command{RFcovmatrix} returns a covariance matrix. Here, a matrix of coordinates (\code{x}) or a vector or a matrix of \code{distances}
is expected.
\command{RFcovmatrix} also allows for variogram models. Then the negative of the
variogram matrix is returned.
}
\value{
\command{RFcovmatrix} returns a covariance matrix.
}
\me
\seealso{
\command{\link{RMmodel}},
\command{\link{RFsimulate}},
\command{\link{RFfit}},
\command{\link{RFfctn}},
\command{\link{RFcalc}},
\command{\link{RFcov}},
\command{\link{RFpseudovariogram}},
\command{\link{RFvariogram}}.
}
\examples{\dontshow{StartExample()}
##################################################
# Example: get covariance matrix C(x_i,x_j)
# at given locations x_i, i=1,...,n
#
# here for an isotropic stationary covariance model
# yields a 4 times 4 covariance matrix of the form
# C(0) C(5) C(3) C(2.5)
# C(5) C(0) C(4) C(2.5)
# C(3) C(4) C(0) C(2.5)
# C(2.5) C(2.5) C(2.5) C(0)
model <- RMexp() # the covariance function C(x,y)=C(r) of this model
# depends only on the distance r between x and y
RFcovmatrix(model=model, distances=c(5,3,2.5,4,2.5,2.5), dim=4)
\dontshow{FinalizeExample()}}
\keyword{spatial}
|
