# intensity.ppm

##### Intensity of Fitted Point Process Model

Computes the intensity of a fitted point process model.

##### 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, 2012).

##### Value

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

##### References

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

##### See Also

##### Examples

```
fitP <- ppm(swedishpines, ~1, Poisson())
intensity(fitP)
fitS <- ppm(swedishpines, ~1, Strauss(9))
intensity(fitS)
```

*Documentation reproduced from package spatstat, version 1.28-1, License: GPL (>= 2)*