Kernel density estimation of a spatio-temporal point pattern on a linear network.
# S3 method for stlpp
density(x,lbw,tbw,at=c("points","pixels"),dimt=512,...)If at="points": a vector of intensity values at the data points of x.
If at="pixels": a list of images on linear network. Each image represents an estimated spatio-temporal intensity at a fixed time.
Check the attributes for more accommodated outputs.
an object of class stlpp
network smoothing bandwidth
time smoothing bandwidth
string specifying whether to compute the intensity values at a grid of pixel locations and times (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 density.lpp
Mehdi Moradi <m2.moradi@yahoo.com>
Kernel smoothing is applied to the spatio-temporal point pattern x using methods in Moradi et al (2019). The function computes estimated intensities assuming first-order separability. Estimated intensity values of the marginal spatial point pattern on the linear network will be obtained using the fast kernel smoothing technique of Rakshit et al. (2019) and function densityQuick.lpp, whereas the estimated intensity values of the marginal temporal point pattern will be estimated using the function density.
If lbw and tbw are not given, then they will be selected using bw.nrd0 and bw.scott.iso respectively.
Moradi, M., & Mateu, J. (2020). First-and second-order characteristics of spatio-temporal point processes on linear networks. Journal of Computational and Graphical Statistics, 29(3), 432-443.
X <- rpoistlpp(.2,a=0,b=5,L=easynet)
density(X)
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