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.

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

lll{ 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 nobs nobs.ppm number of observations }

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

ll{ name description confint Confidence intervals for parameters step Stepwise model selection drop1 One-step model improvement add1 One-step model improvement }

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

lll{ generic method description as.interact as.interact.ppm Interpoint interaction structure as.owin as.owin.ppm Observation window of data bermantest bermantest.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 kstest kstest.ppm Kolmogorov-Smirnov 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 unitname unitname.ppm Name of unit of length } Information about the data (to which the model was fitted) can be extracted using data.ppm, dummy.ppm and quad.ppm.

Internal format

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 of ppm objects may change slightly between releases of the spatstat package.

See Also

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

Aliases
  • ppm.object
  • methods.ppm
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 1.31-3, License: GPL (>= 2)

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