| densityVoronoi.stlpp {stlnpp} | R Documentation |
Intensity estimate of spatio-temporal point pattern using Voronoi-Dirichlet tessellation
Description
This function performs adaptive intensity estimation for spatio-temporal point patterns on linear networks using Voronoi-Dirichlet tessellation.
Usage
## S3 method for class 'stlpp'
densityVoronoi(X, f = 1, nrep = 1, separable=FALSE, at=c("points","pixels"), dimt=128,...)
Arguments
X |
an object of class |
f |
fraction (between 0 and 1 inclusive) of the data points that will be used to build a tessellation for the intensity estimate |
nrep |
number of independent repetitions of the randomised procedure |
separable |
logical. If FALSE, it then calculates a pseudo-separable estimate |
at |
string specifying whether to compute the intensity values at a grid of pixel locations and time (at="pixels") or only at the points of x (at="points"). default is to estimate the intensity at pixels |
dimt |
the number of equally spaced points at which the temporal density is to be estimated. see density |
... |
arguments passed to |
Details
This function computes intensity estimates for spatio-temporal point patterns on linear networks using Voronoi-Dirichlet tessellation. Both first-order separability and pseudo-separability assumptions are accommodated in the function.
If separable=TRUE, the estimated intensities will be a product of the estimated intensities on the network and those on time. Estimated intensity of the spatial component will be obtained using densityVoronoi.lpp, whereas estimated intensities of the temporal component will be obtained via densityVoronoi.tpp. If f=1, the function calculates the estimations based on the original Voronoi intensity estimator.
If separable=FALSE, the estimated intensities will be calculated based on a sub-sampling technique explained in Mateu et al. (2019). nrep sub-samples will be obtained from X based on a given retention probability f, the function densityVoronoi.stlpp, considering separable=TRUE and f=1, will be applied to each obtained sub-sample, and finally, the estimated intensities will be the sum of all obtained estimated intensities from all sub-samples divided by the (f * nrep).
Value
If at="points": a vector of intensity values at the data points of X.
If at="pixels": a list of images on a linear network. Each image represents an estimated spatio-temporal intensity at a fixed time.
Author(s)
Mehdi Moradi <m2.moradi@yahoo.com> and Ottmar Cronie
References
Mateu, J., Moradi, M., & Cronie, O. (2019). Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation. Spatial Statistics, 100400.
See Also
densityVoronoi.lpp, density.stlpp
Examples
X <- rpoistlpp(.2,a=0,b=5,L=easynet)
densityVoronoi(X)