Nothing
#
# localK.R Getis-Franklin neighbourhood density function
#
# $Revision: 1.25 $ $Date: 2019/06/23 06:30:55 $
#
#
"localL" <-
function(X, ..., rmax = NULL, correction="Ripley", verbose=TRUE, rvalue=NULL)
{
localK(X, wantL=TRUE, rmax = rmax,
correction=correction, verbose=verbose, rvalue=rvalue)
}
"localLinhom" <-
function(X, lambda=NULL, ..., rmax = NULL, correction="Ripley",
verbose=TRUE, rvalue=NULL,
sigma=NULL, varcov=NULL, update=TRUE, leaveoneout=TRUE)
{
localKinhom(X, lambda=lambda, wantL=TRUE, ..., rmax = rmax,
correction=correction, verbose=verbose, rvalue=rvalue,
sigma=sigma, varcov=varcov,
update=update, leaveoneout=leaveoneout)
}
"localK" <-
function(X, ..., rmax = NULL, correction="Ripley", verbose=TRUE, rvalue=NULL)
{
verifyclass(X, "ppp")
localKengine(X, ..., rmax = rmax,
correction=correction, verbose=verbose, rvalue=rvalue)
}
"localKinhom" <-
function(X, lambda=NULL, ...,
rmax = NULL, correction="Ripley", verbose=TRUE, rvalue=NULL,
sigma=NULL, varcov=NULL, update=TRUE, leaveoneout=TRUE)
{
verifyclass(X, "ppp")
a <- resolve.lambda(X, lambda, ...,
sigma=sigma, varcov=varcov,
update=update, leaveoneout=leaveoneout)
result <- localKengine(X, lambda=a$lambda, ..., rmax = rmax,
correction=correction,
verbose=verbose, rvalue=rvalue)
if(a$danger)
attr(result, "dangerous") <- a$dangerous
return(result)
}
"localKengine" <-
function(X, ..., wantL=FALSE, lambda=NULL, rmax = NULL,
correction="Ripley", verbose=TRUE, rvalue=NULL)
{
npts <- npoints(X)
W <- X$window
areaW <- area(W)
lambda.ave <- npts/areaW
lambda1.ave <- (npts - 1)/areaW
weighted <- !is.null(lambda)
if(is.null(rvalue))
rmaxdefault <- rmax %orifnull% rmax.rule("K", W, lambda.ave)
else {
stopifnot(is.numeric(rvalue))
stopifnot(length(rvalue) == 1)
stopifnot(rvalue >= 0)
rmaxdefault <- rvalue
}
breaks <- handle.r.b.args(NULL, NULL, W, rmaxdefault=rmaxdefault)
r <- breaks$r
rmax <- breaks$max
correction.given <- !missing(correction)
correction <- pickoption("correction", correction,
c(none="none",
isotropic="isotropic",
Ripley="isotropic",
trans="translate",
translate="translate",
translation="translate",
best="best"),
multi=FALSE)
correction <- implemented.for.K(correction, W$type, correction.given)
# recommended range of r values
alim <- c(0, min(rmax, rmaxdefault))
# identify all close pairs
rmax <- max(r)
close <- closepairs(X, rmax)
DIJ <- close$d
XI <- ppp(close$xi, close$yi, window=W, check=FALSE)
I <- close$i
if(weighted) {
J <- close$j
lambdaJ <- lambda[J]
weightJ <- 1/lambdaJ
}
# initialise
df <- as.data.frame(matrix(NA, length(r), npts))
labl <- desc <- character(npts)
if(verbose) state <- list()
switch(correction,
none={
# uncorrected! For demonstration purposes only!
for(i in 1:npts) {
ii <- (I == i)
wh <- whist(DIJ[ii], breaks$val,
if(weighted) weightJ[ii] else NULL) # no edge weights
df[,i] <- cumsum(wh)
icode <- numalign(i, npts)
names(df)[i] <- paste("un", icode, sep="")
labl[i] <- makefvlabel(NULL, "hat", character(2), icode)
desc[i] <- paste("uncorrected estimate of %s",
"for point", icode)
if(verbose) state <- progressreport(i, npts, state=state)
}
if(!weighted) df <- df/lambda1.ave
},
translate={
# Translation correction
XJ <- ppp(close$xj, close$yj, window=W, check=FALSE)
edgewt <- edge.Trans(XI, XJ, paired=TRUE)
if(weighted)
edgewt <- edgewt * weightJ
for(i in 1:npts) {
ii <- (I == i)
wh <- whist(DIJ[ii], breaks$val, edgewt[ii])
Ktrans <- cumsum(wh)
df[,i] <- Ktrans
icode <- numalign(i, npts)
names(df)[i] <- paste("trans", icode, sep="")
labl[i] <- makefvlabel(NULL, "hat", character(2), icode)
desc[i] <- paste("translation-corrected estimate of %s",
"for point", icode)
if(verbose) state <- progressreport(i, npts, state=state)
}
if(!weighted) df <- df/lambda1.ave
h <- diameter(W)/2
df[r >= h, ] <- NA
},
isotropic={
# Ripley isotropic correction
edgewt <- edge.Ripley(XI, matrix(DIJ, ncol=1))
if(weighted)
edgewt <- edgewt * weightJ
for(i in 1:npts) {
ii <- (I == i)
wh <- whist(DIJ[ii], breaks$val, edgewt[ii])
Kiso <- cumsum(wh)
df[,i] <- Kiso
icode <- numalign(i, npts)
names(df)[i] <- paste("iso", icode, sep="")
labl[i] <- makefvlabel(NULL, "hat", character(2), icode)
desc[i] <- paste("Ripley isotropic correction estimate of %s",
"for point", icode)
if(verbose) state <- progressreport(i, npts, state=state)
}
if(!weighted) df <- df/lambda1.ave
h <- diameter(W)/2
df[r >= h, ] <- NA
})
# transform values if L required
if(wantL)
df <- sqrt(df/pi)
# return vector of values at r=rvalue, if desired
if(!is.null(rvalue)) {
nr <- length(r)
if(r[nr] != rvalue)
stop("Internal error - rvalue not attained")
return(as.numeric(df[nr,]))
}
# function value table required
# add r and theo
if(!wantL) {
df <- cbind(df, data.frame(r=r, theo=pi * r^2))
if(!weighted) {
fnam <- c("K", "loc")
yexp <- ylab <- quote(K[loc](r))
} else {
fnam <- c("K", "list(inhom,loc)")
ylab <- quote(K[inhom,loc](r))
yexp <- quote(K[list(inhom,loc)](r))
}
} else {
df <- cbind(df, data.frame(r=r, theo=r))
if(!weighted) {
fnam <- c("L", "loc")
yexp <- ylab <- quote(L[loc](r))
} else {
fnam <- c("L", "list(inhom,loc)")
ylab <- quote(L[inhom,loc](r))
yexp <- quote(L[list(inhom,loc)](r))
}
}
desc <- c(desc, c("distance argument r", "theoretical Poisson %s"))
labl <- c(labl, c("r", "{%s[%s]^{pois}}(r)"))
# create fv object
K <- fv(df, "r", ylab, "theo", , alim, labl, desc,
fname=fnam, yexp=yexp)
# default is to display them all
formula(K) <- . ~ r
unitname(K) <- unitname(X)
attr(K, "correction") <- correction
return(K)
}
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