Class of Fitted Point Process Models

Description

A class ppm to represent a fitted stochastic model for a point process. The output of mpl.

Details

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, mpl. There are methods print.ppm, predict.ppm and plot.ppm

A ppm object contains at least the following entries:

coef	the fitted regular parameters (as returned by glm)
trend	the trend formula or NULL
interaction	the point process interaction family (an object of class "interact") or NULL
Q	the quadrature scheme used
maxlogpl	the maximised value of log pseudolikelihood
correction	name of edge correction method used

See mpl for explanation of these concepts. The irregular parameters (e.g. the interaction radius of the Strauss process) are encoded in the interaction entry.

See also (for example) Strauss to understand how to specify a point process model with unknown parameters.

Warnings

The internal representation may change in the next few releases of the spatstat package.

Author(s)

Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf

See Also

mpl, print.ppm, predict.ppm, plot.ppm.

Examples

  library(spatstat)
  data(cells)
  fit <- mpl(cells, ~ x, Strauss(0.1), correction="periodic")
  fit
  pred <- predict(fit)
  pred <- predict(fit, nx=50, ny=50, type="trend")
  
  plot(fit)