https://github.com/cran/RandomFields
Tip revision: 6eca414de4c835af2032db4cae6c05e9cc684529 authored by Martin Schlather on 23 April 2016, 15:04:07 UTC
version 3.1.11
version 3.1.11
Tip revision: 6eca414
RMbiwm.Rd
\name{RMbiwm}
\alias{RMbiwm}
\title{Full Bivariate Whittle Matern Model}
\description{
\command{\link{RMbiwm}} is a bivariate stationary isotropic covariance model
whose corresponding covariance function only depends on the distance
\eqn{r \ge 0}{r \ge 0} between
two points and is given for \eqn{i,j \in \{1,2\}}{i,j = 1,2} by
\deqn{C_{ij}(r)=c_{ij} W_{\nu_{ij}}(r/s_{ij}).}{C_{ij}(r)=c_{ij} W_{\nu_{ij}}(r/s_{ij}).}
Here \eqn{W_\nu} is the covariance of the
\command{\link{RMwhittle}} model.
For constraints on the constants see details.
}
\usage{
RMbiwm(nudiag, nured12, nu, s, cdiag, rhored, c, notinvnu, var,
scale, Aniso, proj)
}
\arguments{
\item{nudiag}{a vector of length 2 of numerical values; each entry
positive; the vector \eqn{(\nu_{11},\nu_{22})}}
\item{nured12}{a numerical value in the interval \eqn{[1,\infty)};
\eqn{\nu_{21}} is calculated as \eqn{0.5 (\nu_{11} + \nu_{22})*\nu_{red}}.}
\item{nu}{alternative to \code{nudiag} and \code{nured12}:
a vector of length 3 of numerical values; each entry
positive; the vector \eqn{(\nu_{11},\nu_{21},\nu_{22})}.
Either
\code{nured} and \code{nudiag}, or \code{nu} must be given.}
\item{s}{a vector of length 3 of numerical values; each entry
positive; the vector \eqn{(s_{11},s_{21},s_{22})}}
\item{cdiag}{a vector of length 2 of numerical values; each entry
positive; the vector \eqn{(c_{11},c_{22})}}
\item{rhored}{a numerical value; in the interval \eqn{[-1,1]}. See
also the Details for the corresponding value of \eqn{c_{12}=c_{21}}.
}
\item{c}{a vector of
length 3 of numerical values;
the vector \eqn{(c_{11},c_{21}, c_{22})}. Either
\code{rhored} and \code{cdiag} or \code{c} must be given.}
\item{notinvnu}{logical or \code{NULL}.
If not given (default) then the formula of the
(\command{\link{RMwhittle}}) model applies.
If logical then the formula for the \command{\link{RMmatern}} model
applies. See there for details.
}
\item{var,scale,Aniso,proj}{optional arguments; same meaning for any
\command{\link{RMmodel}}. If not passed, the above
covariance function remains unmodified.}
}
\details{
Constraints on the constants:
For the diagonal elements we have
\deqn{\nu_{ii}, s_{ii}, c_{ii} > 0.}
For the offdiagonal elements we have
\deqn{s_{12}=s_{21} > 0,}
\deqn{\nu_{12} =\nu_{21} = 0.5 (\nu_{11} + \nu{22}) * \nu_{red}}
for some constant \eqn{\nu_{red} \in [1,\infty)} and
\deqn{c_{12} =c_{21} = \rho_{red} \sqrt{f m c_{11} c_{22}}}
for some constant \eqn{\rho_{red}} in \eqn{[-1,1]}.
The constants \eqn{f} and \eqn{m} in the last equation are given as follows:
\deqn{f = (\Gamma(\nu_{11} + d/2) \Gamma(\nu_{22} + d/2)) / (\Gamma(\nu_{11}) \Gamma(\nu_{22})) * (\Gamma(\nu_{12}) / \Gamma(\nu_{12}+d/2))^2 * ( s_{12}^{2*\nu_{12}} / (s_{11}^{\nu_{11}} s_{22}^{\nu_{22}}) )^2}
where \eqn{\Gamma} is the Gamma function and \eqn{d} is the dimension
of the space.
The constant \eqn{m} is
the infimum of the function \eqn{g} on \eqn{[0,\infty)} where
\deqn{g(t) = (1/s_{12}^2 +t^2)^{2\nu_{12} + d} (1/s_{11}^2 + t^2)^{-\nu_{11}-d/2} (1/s_{22}^2 + t^2)^{-\nu_{22}-d/2}}
(cf. Gneiting, T., Kleiber, W., Schlather, M. (2010), Full
Bivariate Matern Model (Section 2.2))
% For an alternative model see also \command{\link{RMbiwm}}.
}
\value{
\command{\link{RMbiwm}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\references{
\itemize{
\item Gneiting, T., Kleiber, W., Schlather, M. (2010)
Matern covariance functions for multivariate random fields
\emph{JASA}
}
}
\author{Martin Schlather, \email{schlather@math.uni-mannheim.de}
}
\seealso{
\command{\link{RMparswm}},
\command{\link{RMwhittle}},
\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()}
x <- y <- seq(-10, 10, 0.2)
model <- RMbiwm(nudiag=c(0.3, 2), nured=1, rhored=1, cdiag=c(1, 1.5),
s=c(1, 1, 2))
plot(model)
plot(RFsimulate(model, x, y))
\dontshow{FinalizeExample()}
}