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
- 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
"ppm"). Such objects are produced by the maximum
pseudolikelihood fitting algorithm
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.ppm to print a more useful
description of the fitted model.
- A vector containing the fitted coefficients.
require(spatstat) data(cells) poi <- mpl(cells, ~1, Poisson()) coef(poi) # This is the log of the fitted intensity of the Poisson process str <- mpl(cells, ~1, Strauss(r=0.15), rbord=0.15) coef(str) # is a vector of two entries, # "(Intercept)" and "Interaction" # which are respectively log(beta) and log(gamma) # in the usual notation for Strauss(beta, gamma, r)