dens.net.EqualSplit: Adaptive linear netwokr intensity estimator using the...

In kernstadapt: Adaptive Kernel Estimators for Point Process Intensities on Linear Networks

Adaptive linear netwokr intensity estimator using the Okabe-Sugihara equal-split algorithms

Description

Computes an adaptive-bandwidth kernel estimate for the intensity function through the Okabe-Sugihara equal-split algorithms by using binning of the bandwidth values.

Usage

dens.net.EqualSplit(
  X,
  ...,
  weights = NULL,
  bw = NULL,
  ngroups = NULL,
  at = c("pixels", "points"),
  verbose = FALSE
)

Arguments

X	A point pattern on a linear network (an object of class lpp) to be smoothed.
...	Extra arguments passed to densityHeat.lpp.
weights	Optional. Numeric vector of weights associated with the points of X. Weights can be positive, negative or zero.
bw	Numeric vector of spatial smoothing bandwidths for each point in X. By default this is computed using bw.abram.
ngroups	Number of groups in which the bandwidths should be partitioned. If this number is 1, then a classical non-adaptive estimator will be used for the spatial part with a bandwidth selected as the median of the bw.xy vector.
at	String specifying whether to estimate the intensity at a mesh (at = "pixels") or only at the points of X (at = "points").
verbose	Logical value indicating whether to print progress reports for every partition group.

Details

This function computes an adaptive kernel estimate of the intensity on linear networks. It starts from a point pattern X and partition the spatial component to apply a kernel estimator within each cell. The argument bw specifies the smoothing bandwidth vector to be applied to each of the points in X. It should be a numeric vector of bandwidths. The method partition the range of bandwidths into intervals, subdividing the points of the pattern X into sub-patterns according to the bandwidths, and applying fixed-bandwidth smoothing to each sub-pattern. Specifying ngroups = 1 is the same as fixed-bandwidth smoothing with bandwidth sigma = median(bw).

Value

If at = "points" (the default), the result is a numeric vector with one entry for each data point in X. if at = "pixels" is a pixel image on a linear network (linim objects) corresponding to the intensity over linear network.

Author(s)

Jonatan A. González

References

González J.A. and Moraga P. (2018) An adaptive kernel estimator for the intensity function of spatio-temporal point processes http://arxiv.org/pdf/2208.12026

kernstadapt documentation built on Sept. 30, 2024, 9:44 a.m.