Intensity of Fitted Point Process Model

Description

Computes the intensity of a fitted point process model.

Usage

## S3 method for class 'ppm'
intensity(X, ...)

Arguments

Details

This is a method for the generic function intensity for fitted point process models (class "ppm").

The intensity of a point process model is the expected number of random points per unit area.

If X is a Poisson point process model, the intensity of the process is computed exactly. The result is a numerical value if X is a stationary Poisson point process, and a pixel image if X is non-stationary. (In the latter case, the resolution of the pixel image is controlled by the arguments ... which are passed to predict.ppm.)

If X is another Gibbs point process model, the intensity is computed approximately using the Poisson-saddlepoint approximation (Baddeley and Nair, 2012a, 2012b). Currently this is implemented only for pairwise interactions. In the non-stationary case the pseudostationary solution (Baddeley and Nair, 2012b) is used.

Value

A numeric value (if the model is stationary) or a pixel image.

Author(s)

Adrian Baddeley Adrian.Baddeley@uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Gopal Nair.

References

Baddeley, A. and Nair, G. (2012a) Fast approximation of the intensity of Gibbs point processes. Electronic Journal of Statistics 6 1155–1169.

Baddeley, A. and Nair, G. (2012b) Approximating the moments of a spatial point process. Stat 1, 1, 18–30. doi: 10.1002/sta4.5

See Also

intensity, intensity.ppp

Examples

  fitP <- ppm(swedishpines ~ 1)
  intensity(fitP)
  fitS <- ppm(swedishpines ~ 1, Strauss(9))
  intensity(fitS)
  fitSx <- ppm(swedishpines ~ x, Strauss(9))
  lamSx <- intensity(fitSx)