densityVoronoi.stlpp | R Documentation |
adaptive intensity estimation for spatio-temporal point patterns on linear networks using Voronoi-Dirichlet tessellation.
## S3 method for class 'stlpp' densityVoronoi(X, f = 1, nrep = 1, separable=FALSE,at=c("points","pixels"), dimt=128,...)
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 return 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 |
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. Each will be obtained using densityVoronoi.lpp
. 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 subsampling technique explained in Mateu et al. (2019). nrep subsamples 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 subsample, and finally, the estimated intensities will be the sum of all obtained estimated intensities from all subsamples divided by the (f * nrep).
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 saptio-temporal intensity at a fixed time.
Mehdi Moradi <m2.moradi@yahoo.com>, and Ottmar Cronie.
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) densityVoronoi(X)
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