Extract the Interaction from a Fitted Point Process Model
Given a point process model that has been fitted to point pattern data, this function extracts the interpoint interaction part of the model as a separate object.
fitin(object) ## S3 method for class 'ppm' fitin(object) ## S3 method for class 'profilepl' fitin(object)
object |
A fitted point process model (object of class
|
An object of class "ppm" describes a fitted point process
model. It contains information about the original data to which the
model was fitted, the spatial trend that was fitted, the
interpoint interaction that was fitted, and other data.
See ppm.object) for details of this class.
The function fitin extracts from this model the information about the
fitted interpoint interaction only.
The information is organised as an object of class "fii"
(fitted interpoint interaction).
This object can be printed or plotted.
Users may find this a convenient way to plot the fitted interpoint interaction term, as shown in the Examples.
For a pairwise interaction, the plot of the fitted interaction shows the pair interaction function (the contribution to the probability density from a pair of points as a function of the distance between them). For a higher-order interaction, the plot shows the strongest interaction (the value most different from 1) that could ever arise at the given distance.
The fitted interaction coefficients can also be extracted
from this object using coef.
An object of class "fii" representing the fitted
interpoint interaction. This object can be printed and plotted.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Methods for handling fitted interactions:
methods.fii, reach.fii,
as.interact.fii.
Background:
ppm,
ppm.object.
# unmarked model <- ppm(swedishpines ~1, PairPiece(seq(3,19,by=4))) f <- fitin(model) f plot(f) # extract fitted interaction coefficients coef(f) # multitype # fit the stationary multitype Strauss process to `amacrine' r <- 0.02 * matrix(c(1,2,2,1), nrow=2,ncol=2) model <- ppm(amacrine ~1, MultiStrauss(r)) f <- fitin(model) f plot(f)
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