Computes a kernel density estimate on a linear network using the Okabe-Sugihara equal-split algorithms.
densityEqualSplit(x, sigma = NULL, ...,
at = c("pixels", "points"),
leaveoneout=TRUE,
weights = NULL,
kernel = "epanechnikov", continuous = TRUE,
epsilon = 1e-06, verbose = TRUE, debug = FALSE, savehistory = TRUE)If at="pixels" (the default),
a pixel image on the linear network (object of class "linim").
If at="points", a numeric vector with one entry for each point
of x.
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.
The argument sigma specifies the smoothing bandwidth.
If sigma is missing or NULL,
the default is one-eighth of the length of the shortest side
of the bounding box of x.
If sigma is a function in the R language, it is assumed
to be a bandwidth selection rule, and it will be applied to x
to compute the bandwidth value.
Okabe, A. and Sugihara, K. (2012) Spatial analysis along networks. Wiley.
density.lpp
X <- runiflpp(3, simplenet)
De <- density(X, 0.2, kernel="epanechnikov", verbose=FALSE)
Ded <- density(X, 0.2, kernel="epanechnikov", continuous=FALSE, verbose=FALSE)
Run the code above in your browser using DataLab