Analysing spatial point patterns in R - CSIRO

33.3 Fitting Poisson models 209
where α 1 ,... ,α 6 and β 1 ,...,β 6 are parameters. The intensity is loglinear in x with a different
slope and intercept for each mark.
The result of ppm is again an object of class "ppm" representing a fitted point process model.
To plot the fitted intensity and conditional intensity of the fitted model, use plot.ppm. For a
multitype point process you will get a separate plot for each possible mark value.
More complicated examples are:
> ppm(lansing, ~marks * polynom(x, y, 2))
> ppm(lansing, ~marks * harmonic(x, y, 2))
33.3.4 Facilities available
A fitted multitype Poisson process model can be manipulated using any of the methods available
for the class ppm:
print print basic information
summary print detailed summary information
plot plot the fitted (conditional) intensity
predict fitted (conditional) intensity
fitted fitted (conditional) intensity at data points
update re-fit the model
coef extract the fitted coefficient vector ̂θ
vcov variance-covariance matrix of ̂θ
anova analysis of deviance
logLik evaluate log-pseudolikelihood
model.matrix extract design matrix
formula extract trend formula of model
terms extract terms in model formula
The following functions are also available:
step stepwise model selection
drop1 one step backward in model selection
model.images compute images of canonical covariates in model
effectfun fitted intensity as function of one covariate
A fitted multitype Poisson process model can be simulated automatically using rmh.ppm or
simulate.ppm.
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