Computes a kernel density estimate on a linear network using the Okabe-Sugihara equal-split algorithms.
densityEqualSplit(x, sigma = NULL, …, weights = NULL,
kernel = "epanechnikov", continuous = TRUE,
epsilon = 1e-06, verbose = TRUE, debug = FALSE, savehistory = TRUE)Point pattern on a linear network (object of class "lpp")
to be smoothed.
Smoothing bandwidth (standard deviation of the kernel)
in the same units as the spatial coordinates of x.
Arguments passed to as.mask determining the
resolution of the result.
Optional. Numeric vector of weights associated with the
points of x. Weights may be positive, negative or zero.
Character string specifying the smoothing kernel.
See dkernel for possible options.
Logical value indicating whether to compute the
“equal-split continuous” smoother (continuous=TRUE, the
default) or the “equal-split discontinuous” smoother
(continuous=FALSE).
Tolerance value. A tail of the kernel with total mass
less than epsilon may be deleted.
Logical value indicating whether to print progress reports.
Logical value indicating whether to print debugging information.
Logical value indicating whether to save the entire history of the algorithm, for the purposes of evaluating performance.
A pixel image on the linear network (object of class "linim").
If sigma=Inf, the resulting density estimate is
constant over all locations,
and is equal to the average density of points per unit length.
(If the network is not connected, then this rule
is applied separately to each connected component of the network).
Kernel smoothing is applied to the points of x
using a kernel based on path distances in the network.
The result is a pixel image on the linear network (class
"linim") which can be plotted.
Smoothing is performed using one of the “equal-split” rules described in Okabe and Sugihara (2012).
If continuous=TRUE (the default), smoothing is performed
using the “equal-split continuous” rule described in
Section 9.2.3 of Okabe and Sugihara (2012).
The resulting function is continuous on the linear network.
If continuous=FALSE, smoothing is performed
using the “equal-split discontinuous” rule described in
Section 9.2.2 of Okabe and Sugihara (2012). The
resulting function is not continuous.
Computation is performed by path-tracing as described in Okabe and Sugihara (2012).
It is advisable to choose a kernel with bounded support
such as kernel="epanechnikov".
With a Gaussian kernel, computation time can be long, and
increases exponentially with sigma.
Faster algorithms are available through density.lpp.
Okabe, A. and Sugihara, K. (2012) Spatial analysis along networks. Wiley.
# NOT RUN {
X <- runiflpp(3, simplenet)
De <- density(X, 0.2, kernel="epanechnikov", verbose=FALSE)
Ded <- density(X, 0.2, kernel="epanechnikov", continuous=FALSE, verbose=FALSE)
# }
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