spatstat (version 1.8-8)

density.ppp: Kernel Smoothed Intensity of Point Pattern

Description

Compute a kernel smoothed intensity function from a point pattern.

Usage

density.ppp(x, sigma, ..., weights, edge=TRUE)

Arguments

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.

Value

• A pixel image (object of class "im").

synopsis

density.ppp(x, sigma, ..., edge=TRUE)

Details

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.

Computation is performed using the Fast Fourier Transform.

See Also

ppp.object, im.object

Examples

data(cells)
  Z <- density.ppp(cells, 0.05)
  plot(Z)

Run the code above in your browser using DataCamp Workspace