spatialkernel-package: The Spatialkernel Package

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

Description

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 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").

Maintainer

Pingping Zheng [e-mail]

Note

For the convience of the user, we present here examples which show how to use some of the functions in the package.

Author(s)

Pingping Zheng and Peter Diggle

References

1. P. Zheng, P.A. Durr and P.J. Diggle (2004) Edge-correction for Spatial Kernel Smoothing — When Is It Necessary? Proceedings of the GisVet Conference 2004, University of Guelph, Ontario, Canada, June 2004.

2. 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. J. R. Stat. Soc. C, 54, 3, 645–658.

See Also

cvloglk, phat, mcseg.test, plotphat, plotmc, pinpoly, risk.colors, metre

Examples

## An example of spatial segregation analysis
  ## 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
  #In a real application you may want to set 'length' to a higher value.
  h <- seq(0.02, 0.1, length=11)
  cv <- cvloglk(pts, marks, h=h)$cv
  hcv <- h[which.max(cv)]
  plot(h, cv, type="l")
  ## estimate type-specific probabilities and do segregation tests
  ## by one integrated function
  #
  # In a real application, set 'ntest' to 99 or a larger number.
  sp <- spseg(pts, marks, hcv, opt=3, ntest=5, poly=polyb)
  ## plot estimated type-specific probability surfaces
  plotphat(sp)
  ## additional with pointwise significance contour lines
  plotmc(sp, quan=c(0.025, 0.975))
  ## p-value of the Monte Carlo segregation test
  cat("\np-value of the Monte Carlo segregation test", sp$pvalue)
  
  ##estimate intensity function at grid point for presentation
  ##with bandwidth hcv
  gridxy <- as.matrix(expand.grid(x=seq(0, 1, length=41), y=seq(0, 1, length=41)))
  ndx <- which(pinpoly(polyb, gridxy) > 0) ##inside point index
  lam <- matrix(NA, ncol = 41, nrow = 41)
  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=41), y=seq(0, 1, length=41),
    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=51)
  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")

Example output

This is spatialkernel 0.4-23



Calculating type-specific probabilities

Monte Carlo testing

Processing No. 1 out of 5
Processing No. 2 out of 5
Processing No. 3 out of 5
Processing No. 4 out of 5
Processing No. 5 out of 5

p-value of the Monte Carlo segregation test 0.2NULL

spatialkernel documentation built on May 2, 2019, 2:26 p.m.