ppm.object
Class of Fitted Point Process Models
A class ppm
to represent a fitted stochastic model
for a point process. The output of ppm
.
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 
Onestep 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
.
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)
# }