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

.

```
# S3 method for ppm
coef(object, …)
```

object

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

)

…

Ignored.

A vector containing the fitted coefficients.

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.

# NOT RUN { 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)) coef(stra) # The two entries "(Intercept)" and "Interaction" # are respectively log(beta) and log(gamma) # in the usual notation for Strauss(beta, gamma, r) # }