Class of Fitted Point Process Models
ppm to represent a fitted stochastic model
for a point process. The output of
An object of class
ppm represents a stochastic point process
model that has been fitted to a point pattern dataset.
Typically it is the output of the maximum pseudolikelihood model fitter,
See also (for example)
to understand how to specify
a point process model with unknown parameters.
If you really need to get at the internals,
ppm object contains at least the following entries:
coef the fitted regular parameters (as returned by
trend the trend formula or
interaction the point process interaction family
(an object of class
Q the quadrature scheme used
maxlogpl the maximised value of log pseudolikelihood
correction name of edge correction method used
mpl for explanation of these concepts.
The irregular parameters (e.g. the interaction radius of the
Strauss process) are encoded in the
However see the Warnings.
The internal representation may change in the next few releases
library(spatstat) data(cells) fit <- mpl(cells, ~ x, Strauss(0.1), correction="periodic") fit coef(fit) pred <- predict(fit) pred <- predict(fit, nx=50, ny=50, type="trend") plot(fit)