This function performs adaptive intensity estimation for temporal point patterns using Voronoi-Dirichlet tessellation.
# S3 method for tpp
densityVoronoi(X, f = 1, nrep = 1, at=c("points","pixels"), dimt=128,...)If at="points": a vector of intensity values at the data points of X.
If at="pixels": a vector of intensity values over a grid.
an object of class tpp
fraction (between 0 and 1 inclusive) of the data points that will be used to build a tessellation for the intensity estimate
number of independent repetitions of the randomised procedure
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
the number of equally spaced points at which the temporal density is to be estimated. see density
arguments passed to densityVoronoi.lpp
Mehdi Moradi <m2.moradi@yahoo.com> and Ottmar Cronie
This function computes intensity estimates for temporal point patterns using Voronoi-Dirichlet tessellation.
IF f<1, then nrep independent sub-samples of X are obtained using the function rthin.stlpp. Then for each of the obtained sub-samples, we calculate the Voronoi estimate. The final estimation is the sum of all obtained estimated intensities divided by (f*nrep).
Mateu, J., Moradi, M., & Cronie, O. (2019). Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation. Spatial Statistics, 100400.
densityVoronoi.lpp, density.stlpp
X <- rpoistlpp(0.2,a=0,b=5,L=easynet)
Y <- as.tpp.stlpp(X)
densityVoronoi(Y)
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