# intensity.ppm

0th

Percentile

##### Intensity of Fitted Point Process Model

Computes the intensity of a fitted point process model.

Keywords
models, spatial
##### Usage
## S3 method for class 'ppm':
intensity(X, ...)
##### Arguments
X
A fitted point process model (object of class "ppm").
...
Arguments passed to predict.ppm in some cases. See Details.
##### 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.

##### 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

intensity, intensity.ppp

##### Aliases
• intensity.ppm
##### Examples
fitP <- ppm(swedishpines ~ 1)
intensity(fitP)
fitS <- ppm(swedishpines ~ 1, Strauss(9))
intensity(fitS)
fitSx <- ppm(swedishpines ~ x, Strauss(9))
lamSx <- intensity(fitSx)
Documentation reproduced from package spatstat, version 1.37-0, License: GPL (>= 2)

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