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)