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#'
#' densityAdaptiveKernel.ppp.R
#'
#' $Revision: 1.16 $ $Date: 2024/06/04 03:09:11 $
#'
#'
#' Adaptive kernel smoothing via 3D FFT
#'
densityAdaptiveKernel.ppp <- function(X, bw, ...,
weights=NULL,
at=c("pixels", "points"),
edge=TRUE,
ngroups) {
stopifnot(is.ppp(X))
at <- match.arg(at)
nX <- npoints(X)
if(nX == 0)
switch(at,
points = return(numeric(nX)),
pixels = return(as.im(0, W=Window(X), ...)))
if(missing(ngroups) || is.null(ngroups)) {
## default rule
ngroups <- max(1L, floor(sqrt(nX)))
} else if(any(is.infinite(ngroups))) {
ngroups <- nX
} else {
check.1.integer(ngroups)
ngroups <- min(nX, ngroups)
}
if(weighted <- !is.null(weights)) {
check.nvector(weights, nX, oneok=TRUE, vname="weights")
if(length(weights) == 1) weights <- rep(weights, nX)
} else weights <- rep(1,nX)
## determine bandwidth for each data point
if(missing(bw) || is.null(bw)) {
bw <- do.call.matched(bw.abram,
resolve.defaults(list(X=quote(X), at="points"),
list(...)),
extrargs=names(args(as.mask)))
} else if(is.numeric(bw)) {
check.nvector(bw, nX, oneok=TRUE, vname="bw")
if(length(bw) == 1) bw <- rep(bw, nX)
} else if(is.im(bw)) {
bw <- safelookup(bw, X, warn=FALSE)
if(anyNA(bw))
stop("Some data points lie outside the domain of image 'bw'",
call.=FALSE)
} else if(inherits(bw, "funxy")) {
bw <- bw(X)
if(anyNA(bw))
stop("Some data points lie outside the domain of function 'bw'",
call.=FALSE)
} else stop("Argument 'bw' should be a numeric vector or a pixel image")
#' divide bandwidths into groups
if(ngroups == nX) {
## every data point is a separate group
groupid <- 1:nX
qmid <- bw
} else {
## usual case
p <- seq(0,1,length=ngroups+1)
qbands <- quantile(bw, p)
groupid <- findInterval(bw,qbands,all.inside=TRUE)
#' map to middle of group
pmid <- (p[-1] + p[-length(p)])/2
qmid <- quantile(bw, pmid)
}
marks(X) <- if(weighted) weights else NULL
group <- factor(groupid, levels=1:ngroups)
Y <- split(X, group)
Z <- mapply(density.ppp,
x=Y,
sigma=as.list(qmid),
weights=lapply(Y, marks),
MoreArgs=list(edge=edge, at=at, ...),
SIMPLIFY=FALSE)
ZZ <- switch(at,
pixels = im.apply(Z, "sum"),
points = unsplit(Z, group))
return(ZZ)
}
densityAdaptiveKernel.ppplist <-
densityAdaptiveKernel.splitppp <- function(X, bw=NULL, ...,
weights=NULL) {
n <- length(X)
bw <- ensure.listarg(bw,
n=n,
singletypes=c("NULL", "im", "funxy"),
xtitle="bw")
weights <- ensure.listarg(weights,
n=n,
singletypes=c("NULL", "im", "funxy", "expression"),
xtitle="weights")
y <- mapply(densityAdaptiveKernel.ppp, X=X, bw=bw, weights=weights,
MoreArgs=list(...),
SIMPLIFY=FALSE)
return(as.solist(y, demote=TRUE))
}
## move this to spatstat.data when stable
ensure.listarg <- function(x, n, singletypes=character(0),
xtitle=NULL, things="point patterns") {
if(inherits(x, singletypes)) {
## single object: replicate it
x <- rep(list(x), n)
return(x)
}
if(!is.list(x)) {
## error
if(is.null(xtitle)) xtitle <- short.deparse(substitute(x))
whinge <- paste(xtitle, "should be a list")
if(length(singletypes)) {
otypes <- setdiff(singletypes, "NULL")
if(length(otypes))
whinge <- paste(whinge,
"or an object of class",
commasep(dQuote(otypes), "or"))
if("NULL" %in% singletypes)
whinge <- paste(whinge, "or NULL")
}
stop(whinge, call.=FALSE)
}
nx <- length(x)
if(nx != n) {
if(is.null(xtitle)) xtitle <- short.deparse(substitute(x))
whinge <- paste("The length of",
sQuote(xtitle),
"should equal the number of",
things,
paren(paste(nx, "!=", n)))
stop(whinge, call.=FALSE)
}
return(x)
}
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