| density.ppp {spatstat} | R Documentation |
Compute a kernel smoothed intensity function from a point pattern.
## S3 method for class 'ppp': density(x, sigma, ..., weights, edge=TRUE, varcov=NULL)
x |
Point pattern (object of class "ppp") to be smoothed.
|
sigma |
Standard deviation of isotropic Gaussian smoothing kernel. |
weights |
Optional vector of weights to be attached to the points. May include negative values. |
... |
Arguments passed to as.mask to determine
the pixel resolution.
|
edge |
Logical flag: if TRUE, apply edge correction.
|
varcov |
Variance-covariance matrix of anisotropic Gaussian kernel.
Incompatible with sigma.
|
This is a method for the generic function density.
A kernel estimate of the intensity function of the point pattern
is computed. The result is
the convolution of the isotropic Gaussian kernel of
standard deviation sigma with point masses at each of the data
points. The default is to assign
a unit weight to each point.
If weights is present, the point masses have these
weights (which may be signed real numbers).
If edge=TRUE, the intensity estimate is corrected for
edge effect bias by dividing it by the convolution of the
Gaussian kernel with the window of observation.
Instead of the isotropic Gaussian kernel with standard deviation
sigma, the smoothing kernel may be chosen to be any Gaussian
kernel, by giving the variance-covariance matrix varcov.
The arguments sigma and varcov are incompatible.
Also sigma may be a vector of length 2 giving the
standard deviations of two independent Gaussian coordinates,
thus equivalent to varcov = diag(sigma^2).
Computation is performed using the Fast Fourier Transform.
Accuracy depends on the pixel resolution, controlled by the arguments
... passed to as.mask.
A pixel image (object of class "im").
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
data(cells) Z <- density.ppp(cells, 0.05) plot(Z)