R/spatialkernel-package.R

In spatialkernel: Non-Parametric Estimation of Spatial Segregation in a Multivariate Point Process

#' The Spatialkernel Package
#' 
#' An \R package for spatial point process analysis.
#' @details This package contains functions for spatial point process
#'   analysis using kernel smoothing methods. This package has
#'   been written to be compatible with the \pkg{splancs} package
#'   which is available on \cite{CRAN} (The Comprehensive R Archive Network).
#'   
#'   For a complete list of functions with individual help pages,
#'   use \code{library(help = \ "spatialkernel")}.
#' @author Pingping Zheng and Peter Diggle
#' @section Maintainer:
#'   Pingping Zheng \email{pingping.zheng@lancaster.ac.uk}
#' @references
#' \enumerate{
#'   \item P. Zheng, P.A. Durr and P.J. Diggle (2004) Edge-correction for Spatial
#'   Kernel Smoothing --- When Is It Necessary? \emph{Proceedings of the GisVet
#'     Conference 2004}, University of Guelph, Ontario, Canada, June 2004.
#'   \item Diggle, P.J., Zheng, P. and Durr, P. A. (2005)
#'   Nonparametric estimation of spatial segregation in a multivariate
#'   point process: bovine tuberculosis in Cornwall, UK. \emph{J. R. 
#'     Stat. Soc. C}, \bold{54}, 3, 645--658.
#' }
#' @examples
#' ## An example of spatial segregation analysis
#' \dontrun{
#'   ## source in Lansing Woods tree data within a polygon boundary
#'   data(lansing)
#'   data(polyb)
#'   ## select data points within polygon
#'   ndx <- which(pinpoly(polyb, as.matrix(lansing[c("x", "y")])) > 0)
#'   pts <- as.matrix(lansing[c("x", "y")])[ndx,]
#'   marks <- lansing[["marks"]][ndx]
#'   ## select bandwidth
#'   h <- seq(0.02, 0.1, length=101)
#'   cv <- cvloglk(pts, marks, h=h)$cv
#'   hcv <- h[which.max(cv)]
#'   plot(h, cv, type="l")
#'   ## estimate type-specific probabilities and do segregation tests
#'   ## by one integrated function
#'   sp <- spseg(pts, marks, hcv, opt=3, ntest=99, poly=polyb)
#'   ## plot estimated type-specific probability surfaces
#'   plotphat(sp)
#'   ## additional with pointwise significance contour lines
#'   plotmc(sp, quan=c(0.025, 0.975))
#'   ## p-value of the Monte Carlo segregation test
#'   cat("\np-value of the Monte Carlo segregation test", sp$pvalue)
#'   
#'   ##estimate intensity function at grid point for presentation
#'   ##with bandwidth hcv
#'   gridxy <- as.matrix(expand.grid(x=seq(0, 1, length=101), y=seq(0, 1, length=101)))
#'   ndx <- which(pinpoly(polyb, gridxy) > 0) ##inside point index
#'   lam <- matrix(NA, ncol=101, nrow=101)
#'   lam[ndx] <- lambdahat(pts, hcv, gpts = gridxy[ndx,], poly =
#'       polyb)$lambda
#'   brks <- pretty(range(lam, na.rm=TRUE), n=12)
#'   plot(0, 0, xlim=0:1, ylim=0:1, xlab="x", ylab="y", type="n")
#'   image(x=seq(0, 1, length=101), y=seq(0, 1, length=101),
#'     z=lam, add=TRUE, breaks=brks, col=risk.colors(length(brks)-1))
#'   polygon(polyb)
#'   metre(0, 0.01, 0.05, 0.51, lab=brks, col=risk.colors(length(brks)-1), cex=1)
#'   
#'   ## An example of inhomogeneous intensity function and K function
#'   ## estimated with the same data
#'   s <- seq(0, 0.06, length=101)
#'   lam <- lambdahat(pts, hcv, poly=polyb)$lambda
#'   kin <- kinhat(pts, lam, polyb, s)
#'   plot(kin$s, kin$k-pi*(kin$s)^2, xlab="s", ylab="k-pi*s^2", type="l")
#' }
#' @note For the convience of the user, we present here examples which show
#'   how to use some of the functions in the package.
#' @seealso \code{\link{cvloglk}}, \code{\link{phat}},
#'   \code{\link{mcseg.test}}, \code{\link{plotphat}},
#'   \code{\link{plotmc}}, \code{\link{pinpoly}},
#'   \code{\link{risk.colors}}, \code{\link{metre}}
#' @keywords package
#' @name spatialkernel-package
#' @aliases spatialkernel spatialkernel-package
#' @docType package
NULL