https://github.com/cran/spatstat
Tip revision: 3aca716ce2576a0dab83f08052acd47afed8ee6a authored by Adrian Baddeley on 29 February 2012, 00:00:00 UTC
version 1.25-4
version 1.25-4
Tip revision: 3aca716
pcfinhom.R
#
# pcfinhom.R
#
# $Revision: 1.6 $ $Date: 2011/07/23 02:37:00 $
#
# inhomogeneous pair correlation function of point pattern
#
#
pcfinhom <- function(X, lambda=NULL, ..., r=NULL,
kernel="epanechnikov", bw=NULL, stoyan=0.15,
correction=c("translate", "Ripley"),
renormalise=TRUE,
normpower=1,
reciplambda=NULL,
sigma=NULL, varcov=NULL)
{
verifyclass(X, "ppp")
r.override <- !is.null(r)
win <- X$window
area <- area.owin(win)
npts <- npoints(X)
correction.given <- !missing(correction)
correction <- pickoption("correction", correction,
c(isotropic="isotropic",
Ripley="isotropic",
translate="translate",
best="best"),
multi=TRUE)
correction <- implemented.for.K(correction, win$type, correction.given)
if(is.null(bw) && kernel=="epanechnikov") {
# Stoyan & Stoyan 1995, eq (15.16), page 285
h <- stoyan /sqrt(npts/area)
# conversion to standard deviation
bw <- h/sqrt(5)
}
########## intensity values #########################
if(missing(lambda) && is.null(reciplambda)) {
# No intensity data provided
# Estimate density by leave-one-out kernel smoothing
lambda <- density(X, ..., sigma=sigma, varcov=varcov,
at="points", leaveoneout=TRUE)
lambda <- as.numeric(lambda)
reciplambda <- 1/lambda
} else if(!is.null(reciplambda)) {
# 1/lambda values provided
if(is.im(reciplambda))
reciplambda <- safelookup(reciplambda, X)
else if(is.function(reciplambda))
reciplambda <- reciplambda(X$x, X$y)
else if(is.numeric(reciplambda) && is.vector(as.numeric(reciplambda)))
check.nvector(reciplambda, npts)
else stop(paste(sQuote("reciplambda"),
"should be a vector, a pixel image, or a function"))
} else {
# lambda values provided
if(is.im(lambda))
lambda <- safelookup(lambda, X)
else if(is.ppm(lambda))
lambda <- predict(lambda, locations=X, type="trend")
else if(is.function(lambda))
lambda <- lambda(X$x, X$y)
else if(is.numeric(lambda) && is.vector(as.numeric(lambda)))
check.nvector(lambda, npts)
else stop(paste(sQuote("lambda"),
"should be a vector, a pixel image, or a function"))
# evaluate reciprocal
reciplambda <- 1/lambda
}
# renormalise
if(renormalise) {
check.1.real(normpower)
stopifnot(normpower %in% 1:2)
renorm.factor <- (area/sum(reciplambda))^normpower
}
########## r values ############################
# handle arguments r and breaks
rmaxdefault <- rmax.rule("K", win, lambda)
breaks <- handle.r.b.args(r, NULL, win, rmaxdefault=rmaxdefault)
if(!(breaks$even))
stop("r values must be evenly spaced")
# extract r values
r <- breaks$r
rmax <- breaks$max
# recommended range of r values for plotting
alim <- c(0, min(rmax, rmaxdefault))
########## smoothing parameters for pcf ############################
# arguments for 'density'
from <- 0
to <- max(r)
nr <- length(r)
denargs <- resolve.defaults(list(kernel=kernel, bw=bw),
list(...),
list(n=nr, from=from, to=to))
#################################################
# compute pairwise distances
close <- closepairs(X, max(r))
dIJ <- close$d
I <- close$i
J <- close$j
XI <- ppp(close$xi, close$yi, window=win, check=FALSE)
wIJ <- reciplambda[I] * reciplambda[J]
# initialise fv object
df <- data.frame(r=r, theo=rep(1,length(r)))
out <- fv(df, "r",
substitute(g[inhom](r), NULL), "theo", ,
alim,
c("r","{%s^{Pois}}(r)"),
c("distance argument r", "theoretical Poisson %s"),
fname="g[inhom]")
###### compute #######
if(any(correction=="translate")) {
# translation correction
XJ <- ppp(close$xj, close$yj, window=win, check=FALSE)
edgewt <- edge.Trans(XI, XJ, paired=TRUE)
gT <- sewpcf(dIJ, edgewt * wIJ, denargs, area)$g
if(renormalise) gT <- gT * renorm.factor
out <- bind.fv(out,
data.frame(trans=gT),
"hat(%s^{Trans})(r)",
"translation-corrected estimate of %s",
"trans")
}
if(any(correction=="isotropic")) {
# Ripley isotropic correction
edgewt <- edge.Ripley(XI, matrix(dIJ, ncol=1))
gR <- sewpcf(dIJ, edgewt * wIJ, denargs, area)$g
if(renormalise) gR <- gR * renorm.factor
out <- bind.fv(out,
data.frame(iso=gR),
"hat(%s^{Ripley})(r)",
"isotropic-corrected estimate of %s",
"iso")
}
# sanity check
if(is.null(out)) {
warning("Nothing computed - no edge corrections chosen")
return(NULL)
}
# which corrections have been computed?
nama2 <- names(out)
corrxns <- rev(nama2[nama2 != "r"])
# default is to display them all
attr(out, "fmla") <- deparse(as.formula(paste(
"cbind(",
paste(corrxns, collapse=","),
") ~ r")))
unitname(out) <- unitname(X)
return(out)
}