https://github.com/cran/RandomFields
Tip revision: fd4911aa390fd49ddab92bd139bbbf35422e32e5 authored by Martin Schlather on 06 February 2020, 05:20:37 UTC
version 3.3.8
version 3.3.8
Tip revision: fd4911a
RRrectangular.Rd
\name{RRrectangular}
\alias{RRrectangular}
\title{Random scaling used with balls}
\description{
Approximates an isotropic decreasing density function
by a density function that is isotropic with respect to the \eqn{l_1} norm.
}
\usage{
RRrectangular(phi, safety, minsteplen, maxsteps, parts, maxit,
innermin, outermax, mcmc_n, normed, approx, onesided)
}
\arguments{
\item{phi}{a shape function; it is the user's responsibility that it
is non-negative. See Details.}
\item{safety, minsteplen, maxsteps, parts, maxit, innermin, outermax, mcmc_n}{
Technical arguments to run an algorithm to simulate from this
distribution. See \command{\link{RFoptions}} for the default values.
}
\item{normed}{logical. If \code{FALSE} then the norming constant
\eqn{c} in the Details is set to \eqn{1}.
This affects the values of the density function, the
probability distribution and the quantile function, but not
the simulation of random variables.
}
\item{approx}{logical.
Default is \bold{\code{TRUE}}. If \code{TRUE}
the isotropic distribution with respect to the \eqn{l_1} norm
is returned. If \code{FALSE} then the exact isotropic distribution
with respect to the \eqn{l_2} norm is simulated.
Neither the density function, nor the probability distribution, nor
the quantile function will be available if \code{approx=TRUE}.
}
\item{onesided}{logical.
Only used for univariate distributions.
If \code{TRUE} then the density is assumed to be non-negative only
on the positive real axis. Otherwise the density is assumed to be
symmetric.
}
}
\details{
This model defines an isotropic density function $f$ with respect to the
\eqn{l_1} norm, i.e. \eqn{f(x) = c \phi(\|x\|_{l_1})}
with some function \eqn{\phi}.
Here, \eqn{c} is a norming constant so that the integral of \eqn{f}
equals one.
In case that \eqn{\phi} is monotonically decreasing then rejection sampling
is used, else MCMC.
The function \eqn{\phi} might have a polynomial pole at the origin
and asymptotical decreasing of the form \eqn{x^\beta
exp(-x^\delta)}.
}
\value{
\command{\link{RRrectangular}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\me
\seealso{
\command{\link{RMmodel}},
\command{\link{RRdistr}},
\command{\link{RRgauss}}.
}
\keyword{spatial}
\keyword{models}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
# simulation of Gaussian variables (in a not very straightforward way):
distr <- RRrectangular(RMgauss(), approx=FALSE)
z <- RFrdistr(distr, n=1000000)
hist(z, 200, freq=!TRUE)
x <- seq(-10, 10, 0.1)
lines(x, dnorm(x, sd=sqrt(0.5)))
#creation of random variables whose density is proportional
# to the spherical model:
distr <- RRrectangular(RMspheric(), approx=FALSE)
z <- RFrdistr(distr, n=1000000)
hist(z, 200, freq=!TRUE)
\dontshow{StartExample(reduced=FALSE, save.seed=FALSE)}
x <- seq(-10, 10, 0.01)
lines(x, 4/3 * RFcov(RMspheric(), x))
\dontshow{FinalizeExample()}}