dens.net.heat {kernstadapt} | R Documentation |
Adaptive linear network intensity estimator based on heat kernel
Description
Provides an adaptive-bandwidth kernel estimate for point patterns on linear networks by using binning of the bandwidth values.
Usage
dens.net.heat(
X,
...,
weights = NULL,
bw = NULL,
ngroups = NULL,
at = c("pixels", "points")
)
Arguments
X |
A point pattern on a linear network (an object of class |
... |
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 |
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 ( |
Details
This function computes an adaptive kernel estimate of the intensity on linear networks. It starts from a point pattern X
and partition the 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