Intensity of Fitted Point Process Model
Computes the intensity of a fitted point process model.
## S3 method for class 'ppm': intensity(X, ...)
- A fitted point process model (object of class
- Arguments passed to
predict.ppmin some cases. See Details.
This is a method for the generic function
for fitted point process models (class
The intensity of a point process model is the expected number of random points per unit area.
X is a Poisson point process model, the intensity of the
process is computed exactly.
The result is a numerical value if
is a stationary Poisson point process, and a pixel image if
is non-stationary. (In the latter case, the resolution of the pixel
image is controlled by the arguments
... which are passed
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.
- A numeric value (if the model is stationary) or a pixel image.
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
fitP <- ppm(swedishpines ~ 1) intensity(fitP) fitS <- ppm(swedishpines ~ 1, Strauss(9)) intensity(fitS) fitSx <- ppm(swedishpines ~ x, Strauss(9)) lamSx <- intensity(fitSx)