spatialkernel-package {spatialkernel} | R Documentation |
An R package for spatial point process analysis.
This package contains functions for spatial point process analysis using kernel smoothing methods. This package has been written to be compatible with the splancs package which is available on CRAN (The Comprehensive R Archive Network).
For a complete list of functions with individual help pages,
use library(help = \ "spatialkernel")
.
Pingping Zheng pingping.zheng@lancaster.ac.uk
For the convience of the user, we present here examples which show how to use some of the functions in the package.
Pingping Zheng and Peter Diggle
cvloglk
, phat
,
mcseg.test
, plotphat
,
plotmc
, pinpoly
,
risk.colors
, metre
## An example of spatial segregation analysis ## Not run: ## 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)$cv hcv <- h[which.max(cv)] plot(h, cv, type="l") ## estimate type-specific probabilities and do segregation tests ## by one integrated function sp <- spseg(pts, marks, hcv, opt=3, ntest=1000, poly=polyb) ## plot estimated type-specific probability surfaces plotphat(sp) ## additional with pointwise significance contour lines plotmc(sp, quan=c(0.025, 0.975)) ## p-value of the Monte Carlo segregation test cat("\np-value of the Monte Carlo segregation test", sp$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") ## End(Not run)