# coef.mppm

##### Coefficients of Point Process Model Fitted to Multiple Point Patterns

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

.

##### Usage

```
## S3 method for class 'mppm':
coef(object, \dots)
```

##### Arguments

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

) - ...
- 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 `"mppm"`

) produced by the
fitting algorithm `mppm`

). This represents a
point process model that has been fitted
to a list of several point pattern datasets. See `mppm`

for information.

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.mppm`

to print a more useful
description of the fitted model.

##### Value

- A vector containing the fitted coefficients.

##### See Also

##### Examples

```
H <- hyperframe(X=waterstriders)
fit.Poisson <- mppm(X ~ 1, H)
coef(fit.Poisson)
# The single entry "(Intercept)"
# is the log of the fitted intensity of the Poisson process
fit.Strauss <- mppm(X~1, H, Strauss(7))
coef(fit.Strauss)
# 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 1.41-1, License: GPL (>= 2)*