estimate.intensity.point: Kernel intensity estimates of a spatio-temporal point process...

In SepTest: Tests for First-Order Separability in Spatio-Temporal Point Processes

Kernel intensity estimates of a spatio-temporal point process at observed points and its components, and test statistics for first-order separability

Description

Computes kernel-based spatial, temporal, separable, and non-separable intensity estimates evaluated at the observed spatio-temporal event locations. The function also returns the separability diagnostic S_i and global deviation measures quantifying departures from first-order separability.

Usage

estimate.intensity.point(X, n.grid, edge)

Arguments

X	Numeric matrix/data.frame with three columns (x,y,t) giving event coordinates.
n.grid	Integer. Included for API compatibility with grid-based routines; not used.
edge	List with components bw (length 3), space, and time. space and time are Gaussian edge-correction masses evaluated at each event; each may be a scalar or a numeric vector of length nrow(X).

Details

Pairwise Gaussian kernel weights are computed in each dimension and diagonal entries are set to zero to remove self-contributions.

Value

A list with components S.fun, deviation measures, and estimated intensity components at the observed points.

See Also

dnorm

Examples

X <- cbind(stats::runif(50), stats::runif(50), stats::runif(50))
edge <- list(bw = c(0.1, 0.1, 0.1), space = 1, time = 1)
res <- estimate.intensity.point(X, n.grid = 50, edge = edge)
str(res)

SepTest documentation built on Feb. 3, 2026, 5:07 p.m.