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:

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

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:

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
# NOT RUN {
  data(cells)
  fit <- ppm(cells, ~ x, Strauss(0.1), correction="periodic")
  fit
  coef(fit)
  
# }
# NOT RUN {
  pred <- predict(fit)
  
# }
# NOT RUN {
  pred <- predict(fit, ngrid=20, type="trend")
  
# }
# NOT RUN {
  plot(fit)
  
# }
Documentation reproduced from package spatstat, version 1.57-1, License: GPL (>= 2)

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