# ppm.object

0th

Percentile

##### Class of Fitted Point Process Models

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

Keywords
spatial, attribute
##### 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 model fitter, ppm.

There are methods print.ppm, plot.ppm, predict.ppm, fitted.ppm and coef.ppm for the generic functions print, plot, predict, fitted and coef respectively.

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

Information about the data (to which the model was fitted) can be extracted using data.ppm, dummy.ppm and quad.ppm.

If you really need to get at the internals, a ppm object contains at least the following entries: ll{ 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 ppm for explanation of these concepts. The irregular parameters (e.g. the interaction radius of the Strauss process) are encoded in the interaction entry. However see the Warnings.

##### Warnings

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

ppm, coef.ppm, fitted.ppm, print.ppm, predict.ppm, plot.ppm.

• ppm.object
##### Examples
data(cells)
fit <- ppm(cells, ~ x, Strauss(0.1), correction="periodic")
fit
coef(fit)
pred <- predict(fit)
pred <- predict(fit, ngrid=20, type="trend")
plot(fit)
Documentation reproduced from package spatstat, version 1.11-7, License: GPL version 2 or newer

### Community examples

Looks like there are no examples yet.