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\name{RRgauss}
\alias{RRgauss}
\title{Vector Of Independent Gaussian Random Variables}
\description{
\command{RRgauss} defines
the d-dimensional vector of independent Gaussian random
variables.
}
\usage{
RRgauss(mu, sd, log)
}
\arguments{
\item{mu, sd, log}{see \link[stats]{Normal}. Here, the components can be
vectors, leading to multivariate distibution with independent components.}
}
\value{
\command{\link{RRgauss}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\details{
It has the same effect as
\code{\link{RRdistr}(\link[=rnorm]{norm}(mu=mu, sd=sd, log=log))}.
}
\me
\seealso{
\command{\link{RMmodel}},
\command{\link{RRdistr}},
\command{\link{RRunif}}.
Do not mix up \command{RRgauss} with \command{\link{RMgauss}} or
\command{\link{RPgauss}}.
}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
r <- RFrdistr(RRgauss(mu=c(1,5)), n=1000, dim=2)
plot(r[1,], r[2, ])
\dontshow{FinalizeExample()}}
\keyword{spatial}
\keyword{models}
|
