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
## S3 method for class 'mppm': coef(object, \dots)
- The fitted point process model (an object of class
This function is a method for the generic function
object must be a fitted point process model
(object of class
"mppm") produced by the
mppm). This represents a
point process model that has been fitted
to a list of several point pattern datasets. See
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,
coef(object) will yield a single value
"(Intercept)") which is the logarithm of the
fitted intensity of the Poisson process.
print.mppm to print a more useful
description of the fitted model.
- A vector containing the fitted coefficients.
data(waterstriders) 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)