gridweights.Rd
\name{gridweights}
\alias{gridweights}
\title{Compute Quadrature Weights Based on Grid Counts}
\description{
Computes quadrature weights for a given set of points,
using the ``counting weights'' for a grid of rectangular tiles.
}
\usage{
gridweights(X, ntile, \dots, window=NULL, verbose=FALSE, npix=NULL, areas=NULL)
}
\arguments{
\item{X}{Data defining a point pattern.}
\item{ntile}{Number of tiles
in each row and column of the rectangular grid.
An integer vector of length 1 or 2.
}
\item{\dots}{Ignored.}
\item{window}{Default window for the point pattern}
\item{verbose}{Logical flag. If \code{TRUE}, information will be printed
about the computation of the grid weights.
}
\item{npix}{Dimensions of pixel grid to use when
computing a digital approximation to the tile areas.
}
\item{areas}{Vector of areas of the tiles, if they are already known.}
}
\value{
Vector of nonnegative weights for each point in \code{X}.
}
\details{
This function computes a set of quadrature weights
for a given pattern of points
(typically comprising both ``data'' and `dummy'' points).
See \code{\link{quad.object}} for an explanation of quadrature
weights and quadrature schemes.
The weights are computed by the ``counting weights'' rule
based on a regular grid of rectangular tiles.
First \code{X} and (optionally) \code{window} are converted into a
point pattern object. Then the bounding rectangle of the window of
the point pattern is
divided into a regular \code{ntile[1] * ntile[2]} grid of rectangular tiles.
The weight attached to a point of \code{X} is the area of the tile
in which it lies, divided by the number of points of \code{X} lying in
that tile.
For non-rectangular windows the tile areas are currently calculated
by approximating the window as a binary mask. The accuracy of this
approximation is controlled by \code{npix}, which becomes
the argument \code{dimyx} of \code{\link{as.mask}}.
}
\seealso{
\code{\link{quad.object}},
\code{\link{dirichletWeights}}
}
\examples{
Q <- quadscheme(runifpoispp(10))
X <- as.ppp(Q) # data and dummy points together
w <- gridweights(X, 10)
w <- gridweights(X, c(10, 10))
}
\author{\adrian
and \rolf
}
\keyword{spatial}
\keyword{datagen}