Kernel Smoothing of Line Segment Pattern
Compute a kernel smoothed intensity function from a line segment pattern.
## S3 method for class 'psp'
density(x, sigma, ..., weights=NULL, edge=TRUE,
method=c("FFT", "C", "interpreted"),
at=NULL)x |
Line segment pattern (object of class |
sigma |
Standard deviation of isotropic Gaussian smoothing kernel. |
... |
Extra arguments, including arguments passed to |
weights |
Optional. Numerical weights for each line segment.
A numeric vector, of length equal to the number of segments in
|
edge |
Logical flag indicating whether to apply edge correction. |
method |
Character string (partially matched) specifying the method of
computation. Option |
at |
Optional. An object specifying the locations where density values
should be computed. Either a window (object of class |
This is the method for the generic function density
for the class "psp" (line segment patterns).
A kernel estimate of the intensity of the line segment pattern
is computed. The result is
the convolution of the isotropic Gaussian kernel, of
standard deviation sigma, with the line segments.
The result is computed as follows:
if method="FFT" (the default),
the line segments are discretised
using pixellate.psp, then the Fast Fourier Transform
is used to calculate the convolution. This method is the fastest,
but is slightly less accurate. Accuracy can be improved by
increasing pixel resolution.
if method="C" the exact value of the convolution at the
centre of each pixel is computed analytically using C code;
if method="interpreted",
the exact value of the convolution at the
centre of each pixel is computed analytically using R code.
This method is the slowest.
If edge=TRUE this result is adjusted for edge effects
by dividing it by the convolution of the same Gaussian kernel
with the observation window.
If weights are given, then the contribution from line segment
i is multiplied by the value of weights[i].
If the argument at is given, then it specifies the locations
where density values should be computed.
If at is a window,
then the window is converted to a binary mask
using the arguments ..., and density values are computed at
the centre of each pixel in this mask. The result is a pixel image.
If at is a point pattern, then density values are computed at
each point location, and the result is a numeric vector.
A pixel image (object of class "im")
or a numeric vector.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
L <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) D <- density(L, sigma=0.03) plot(D, main="density(L)") plot(L, add=TRUE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.