intensity_pspline  R Documentation 
This is the main function of the geonet
package.
intensity_pspline
estimates the intensity of a point pattern on a
geometric network employing penalized splines as outlined in Schneble
and Kauermann (2020). In distinction to
density.lpp
from the spatstat.linnet
package,
which provides kernel based tools for intensity estimation of point
patterns on linear networks, intensity_pspline
allows to
incorporate covariates while also estimating the baseline intensity.
Covariates can be either internal or external. External covariates
can also be incorporated as a smooth term using penalized splines
with the same syntax as in gam
.
intensity_pspline( X, ..., formula = ~1, delta = "0", h = "0.5", r = 2, scale = NULL, density = FALSE, verbose = FALSE, control = list() )
X 
A point pattern on a geometric network (object of class

... 
Other arguments. The following arguments must match exactly. 
formula 
A onesided formula (if a twosided formula is supplied, the
left hand side of the formula is ignored). The formula can consist of either
linear terms as in linear models ( 
delta 
The global knot distance δ, a numerical vector of length one. If
not supplied, delta will be chosen properly according to the geometric
network 
h 
The global bin width h, a numerical vector of length one. If
not supplied, 
r 
The order of the penalty of the baseline intensity on the geometric
network, default to a penalty of order 
scale 
A named list which specifies the rescaling of network related covariates. Currently, only x and ycoordinates can be scaled. 
density 

verbose 
If 
control 
A list of optional arguments which control the convergence of the fitting algorithm. See "Details". 
A fitted geometric network (object of class gnppfit
).
Marc Schneble marc.schneble@stat.unimuenchen.de
Schneble, M. and G. Kauermann (2020). Intensity estimation on geometric networks with penalized splines. arXiv preprint arXiv:2002.10270 .
library(geonet) X < runifgn(50, small_gn) delta < 0.2 model < intensity_pspline(X, delta = delta) summary(model) plot(model)
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