# coef.ppm

##### Coefficients of Fitted Point Process Model

Given a point process model fitted to a point pattern,
extract the coefficients of the fitted model.
A method for `coef`

.

- Keywords
- spatial

##### Usage

`coef.ppm(object, ...)`

##### Arguments

- object
- The fitted point process model (an object of class
`"ppm"`

) - ...
- Ignored.

##### Details

This function is a method for the generic function `coef`

.
The argument `object`

must be a fitted point process model
(object of class `"ppm"`

). Such objects are produced by the maximum
pseudolikelihood fitting algorithm `ppm`

).

This function extracts the vector of coefficients of the fitted model. This is the estimate of the parameter vector $\theta$ such that the conditional intensity of the model is of the form $$\lambda(u,x) = \exp(\theta S(u,x))$$ where $S(u,x)$ is a (vector-valued) statistic.

For example, if the model `object`

is the uniform Poisson process,
then `coef(object)`

will yield a single value
(named `"(Intercept)"`

) which is the logarithm of the
fitted intensity of the Poisson process.

Use `print.ppm`

to print a more useful
description of the fitted model.

##### Value

- A vector containing the fitted coefficients.

##### See Also

##### Examples

```
data(cells)
poi <- ppm(cells, ~1, Poisson())
coef(poi)
# This is the log of the fitted intensity of the Poisson process
stra <- ppm(cells, ~1, Strauss(r=0.07), rbord=0.07)
coef(stra)
# The two entries "(Intercept)" and "Interaction"
# are respectively log(beta) and log(gamma)
# in the usual notation for Strauss(beta, gamma, r)
```

*Documentation reproduced from package spatstat, version 1.6-2, License: GPL version 2 or newer*