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#' Direct separable adaptive spatio-temporal intensity estimator
#'
#' Provides an adaptive-bandwidth kernel estimate for spatio-temporal point patterns in a separable fashion, i.e., by multiplying spatial and temporal marginals. This estimation is performed by calculating the classical estimator, i.e., the slowest estimation.
#'
#' @param X A spatial point pattern (an object of class \code{ppp}) with the spatial coordinates of the observations. It may contain marks representing times.
#' @param t A numeric vector of temporal coordinates with equal length to the number of points in \code{X}. This gives the time associated with each spatial point. This argument is not necessary if time marks are provided to the point pattern \code{X}.
#' @param dimyx Spatial pixel resolution. The default is 128 for each axes.
#' @param dimt Temporal bin vector dimension. The default is 128.
#' @param bw.xy Numeric vector of spatial smoothing bandwidths for each point in \code{X}. By default this is computed using \link[spatstat.explore]{bw.abram}.
#' @param bw.t Numeric vector of temporal smoothing bandwidths for each point in \code{t}. By default this is computed using \link{bw.abram.temp}.
#' @param at String specifying whether to estimate the intensity at a mesh (\code{at = "bins"}) or only at the points of \code{X} (\code{at = "points"}).
#'
#' @details
#' This function computes a spatio-temporal adaptive kernel estimate of the intensity in a separable fashion. It starts from a planar point pattern \code{X} and a vector of times \code{t} and uses a direct estimator for each dimension, then it multiplies both components and normalises by the number of points to preserve the mass.
#' The arguments \code{bw.xy} and \code{bw.t} specify the smoothing bandwidth vectors to be applied to each of the points in \code{X} and \code{t}. They should be a numeric vectors of bandwidths.
#' The method partition the range of bandwidths into intervals, subdividing the points of the pattern \code{X} and \code{t} into sub-patterns according to the bandwidths, and applying fixed-bandwidth smoothing to each sub-pattern. Specifying \code{ngroups.xy = 1} is the same as fixed-bandwidth smoothing with bandwidth \code{sigma = median(bw.xy)} in the spatial case and \code{ngroups.t = 1} is the same as fixed-bandwidth smoothing with bandwidth \code{sigma = median(bw.xy)}.
#'
#' @return
#' If \code{at = "points"}, the result is a numeric vector with one entry for each data point in \code{X}. if \code{at = "bins"} is a list named (by time-point) list of pixel images (\link[spatstat.geom]{im} objects) corresponding to the joint spatio-temporal intensity over space at each discretised time bin.
#'
#' @references
#' González J.A. and Moraga P. (2018)
#' An adaptive kernel estimator for the intensity function of spatio-temporal point processes
#' <https://arxiv.org/pdf/2208.12026.pdf>
#'
#' @author Jonatan A. González
#'
#' @examples
#' data(santander)
#' X <- santander[sample.int(200)]
#' stIntensity <- dens.direct.sep(X,
#' dimyx = 32, dimt = 6,
#' at = "bins")
#' plot(spatstat.geom::as.solist(stIntensity[2:4]),
#' main = 'Direct separable example', equal.ribbon = TRUE)
#'
#' @export
dens.direct.sep <- function(X, t = NULL,
dimyx = 128, dimt = 128, #resolution
bw.xy = NULL, bw.t = NULL, #bandwidths
at = c("bins", "points") #at
)
{
verifyclass(X, "ppp")
n <- npoints(X)
if(is.null(t)) t <- marks(X)
t <- checkt(t)
nT <- length(t)
if(length(t) != n)
stop(paste("Length of temporal vector does not match number of spatial observations\n npoints(X) = ",n,"; length(t) = ",length(t), sep = ""))
at <- match.arg(at)
at.s <- switch (at, bins = "pixels", points = "points")
range.t <- range(t)
if (is.null(bw.xy)) {
h0 <- OS(X)
bw.xy <- bw.abram(X, h0)
}
if (missing(bw.t) || is.null(bw.t)) {
bw.t <- bw.abram.temp(t)
}
else if (is.numeric(bw.t)) {
check.nvector(bw.t, nT, oneok = TRUE)
if (length(bw.t) == 1)
bw.t <- rep(bw.t, nT)
}
#Temporal part
PPt <- split(t, f = factor(1:X$n))
Z <- mapply(density.default, x = PPt, bw = bw.t, SIMPLIFY = F,
MoreArgs = list(kernel = "gaussian", n = dimt,
from = range.t[1], to = range.t[2] ))
zx <- Z[[1]]$x
Edge0 <- sapply(bw.t, edge.t, dt = zx, TS = range.t[2], TI = range.t[1])
Ys <- sapply(Z, "[[", 'y') / Edge0
Ys <- apply(Ys, 1, sum)
Mtemporal <- switch(at,
points = approxfun(x = zx, y = Ys)(t),
bins = Ys)
#Spatial part
at.s <- switch (at, bins = "pixels", points = "points")
Mspatial <- densityAdaptiveKernel.ppp(unmark(X), bw = bw.xy, at = at.s,
dimyx = dimyx, edge = T, ngroups = Inf)
Mst <- switch(at,
points = Mtemporal * Mspatial / length(t),
bins = lapply(Mtemporal, function(x) eval.im(Mspatial * x / length(t)))
)
return(Mst)
}
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