https://github.com/cran/spatstat
Tip revision: 4b1b757a8dfaf6fa6dfb3d3e862aeb2abdfe4f00 authored by Adrian Baddeley on 27 July 2009, 19:54:06 UTC
version 1.16-1
version 1.16-1
Tip revision: 4b1b757
Gest.S
#
# Gest.S
#
# Compute estimates of nearest neighbour distance distribution function G
#
# $Revision: 4.23 $ $Date: 2009/07/24 05:27:14 $
#
################################################################################
#
"Gest" <-
"nearest.neighbour" <-
function(X, r=NULL, breaks=NULL, ..., correction=c("rs", "km", "han")) {
verifyclass(X, "ppp")
#
W <- X$window
npoints <-X$n
lambda <- npoints/area.owin(W)
# determine r values
rmaxdefault <- rmax.rule("G", W, lambda)
breaks <- handle.r.b.args(r, breaks, W, rmaxdefault=rmaxdefault)
rvals <- breaks$r
rmax <- breaks$max
zeroes <- rep(0, length(rvals))
# choose correction(s)
correction.given <- !missing(correction) && !is.null(correction)
if(is.null(correction))
correction <- c("rs", "km", "han")
correction <- pickoption("correction", correction,
c(none="none",
border="rs",
rs="rs",
KM="km",
km="km",
Kaplan="km",
han="han",
Hanisch="han",
best="km"),
multi=TRUE)
# compute nearest neighbour distances
nnd <- nndist(X$x, X$y)
# distance to boundary
bdry <- bdist.points(X)
# observations
o <- pmin(nnd,bdry)
# censoring indicators
d <- (nnd <= bdry)
# initialise fv object
df <- data.frame(r=rvals, theo=1-exp(-lambda * pi * rvals^2))
Z <- fv(df, "r", substitute(G(r), NULL), "theo", . ~ r,
c(0,rmax),
c("r", "%s[pois](r)"),
c("distance argument r", "theoretical Poisson %s"),
fname="G")
if("none" %in% correction) {
# UNCORRECTED e.d.f. of nearest neighbour distances: use with care
if(npoints == 0)
edf <- zeroes
else {
hh <- hist(nnd[nnd <= rmax],breaks=breaks$val,plot=FALSE)$counts
edf <- cumsum(hh)/length(nnd)
}
Z <- bind.fv(Z, data.frame(raw=edf), "%s[raw](r)",
"uncorrected estimate of %s", "raw")
}
if("han" %in% correction) {
if(npoints == 0)
G <- zeroes
else {
# uncensored distances
x <- nnd[d]
# weights
a <- eroded.areas(W, rvals)
# calculate Hanisch estimator
h <- hist(x[x <= rmax], breaks=breaks$val, plot=FALSE)$counts
G <- cumsum(h/a)
G <- G/max(G[is.finite(G)])
}
# add to fv object
Z <- bind.fv(Z, data.frame(han=G),
"%s[han](r)",
"Hanisch estimate of %s",
"han")
# modify recommended plot range
attr(Z, "alim") <- range(rvals[G <= 0.9])
}
if(any(correction %in% c("rs", "km"))) {
# calculate Kaplan-Meier and border correction (Reduced Sample) estimators
if(npoints == 0)
result <- data.frame(rs=zeroes, km=zeroes, hazard=zeroes)
else {
result <- km.rs(o, bdry, d, breaks)
result <- as.data.frame(result[c("rs", "km", "hazard")])
}
# add to fv object
Z <- bind.fv(Z, result,
c("%s[bord](r)", "%s[km](r)", "hazard(r)"),
c("border corrected estimate of %s",
"Kaplan-Meier estimate of %s",
"Kaplan-Meier estimate of hazard function lambda(r)"),
"km")
# modify recommended plot range
attr(Z, "alim") <- range(rvals[result$km <= 0.9])
}
nama <- names(Z)
attr(Z, "dotnames") <- rev(nama[!(nama %in% c("r", "hazard"))])
unitname(Z) <- unitname(X)
return(Z)
}