ppm.object: Class of Fitted Point Process Models

Description

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

Arguments

Internal format

If you really need to get at the internals, 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 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 of ppm objects may change slightly between releases of the spatstat package.

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.

The class ppm has methods for the following standard generic functions:

generic	method
description	`print`
`print.ppm`	print details
`plot`	`plot.ppm`
plot fitted model	`predict`
`predict.ppm`	fitted intensity and conditional intensity
`fitted`	`fitted.ppm`
fitted intensity	`coef`
`coef.ppm`	fitted coefficients of model
`anova`	`anova.ppm`
Analysis of Deviance	`formula`
`formula.ppm`	Extract model formula
`terms`	`terms.ppm`
Terms in the model formula	`labels`
`labels.ppm`	Names of estimable terms in the model formula
`residuals`	`residuals.ppm`
Point process residuals	`simulate`
`simulate.ppm`	Simulate the fitted model
`update`	`update.ppm`
Change or refit the model	`vcov`
`vcov.ppm`	Variance/covariance matrix of parameter estimates
`model.frame`	`model.frame.ppm`
Model frame	`model.matrix`
`model.matrix.ppm`	Design matrix
`logLik`	`logLik.ppm`
log pseudo likelihood	`extractAIC`
`extractAIC.ppm`	pseudolikelihood counterpart of AIC

Objects of class ppm can also be handled by the following standard functions, without requiring a special method:

name

description

confint

Confidence intervals for parameters

step

Stepwise model selection

drop1

One-step model improvement

The class ppm also has methods for the following generic functions defined in the spatstat package:

generic	method
description	`as.interact`
`as.interact.ppm`	Interpoint interaction structure
`as.owin`	`as.owin.ppm`
Observation window of data	`berman.test`
`berman.test.ppm`	Berman's test
`envelope`	`envelope.ppm`
Simulation envelopes	`fitin`
`fitin.ppm`	Fitted interaction
`is.marked`	`is.marked.ppm`
Determine whether the model is marked	`is.multitype`
`is.multitype.ppm`	Determine whether the model is multitype
`is.poisson`	`is.poisson.ppm`
Determine whether the model is Poisson	`is.stationary`
`is.stationary.ppm`	Determine whether the model is stationary
`cdf.test`	`cdf.test.ppm`
Spatial distribution test	`quadrat.test`
`quadrat.test.ppm`	Quadrat counting test
`reach`	`reach.ppm`
Interaction range of model	`rmhmodel`
`rmhmodel.ppm`	Model in a form that can be simulated
`rmh`	`rmh.ppm`
Perform simulation	generic

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

Examples

Run this code

  data(cells)
  fit <- ppm(cells, ~ x, Strauss(0.1), correction="periodic")
  fit
  coef(fit)
  ## Not run: 
#   pred <- predict(fit)
#   ## End(Not run)
  pred <- predict(fit, ngrid=20, type="trend")
  ## Not run: 
#   plot(fit)
#   ## End(Not run)

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