Given a point process model fitted to a point pattern on a linear network, compute the fitted intensity of the model at the points of the pattern, or at the points of the quadrature scheme used to fit the model.
# S3 method for lppm
fitted(object, ...,
dataonly = FALSE, new.coef = NULL,
leaveoneout = FALSE)A vector containing the values of the fitted spatial trend.
Entries in this vector correspond to the quadrature points (data or
dummy points) used to fit the model.
The quadrature points can be extracted from object
by union.quad(quad.ppm(object)).
Fitted point process model on a linear network
(object of class "lppm").
Ignored.
Logical value indicating whether to computed fitted intensities
at the points of the original point pattern dataset
(dataonly=TRUE)
or at all the quadrature points of the quadrature scheme
used to fit the model (dataonly=FALSE, the default).
Numeric vector of parameter values to replace the
fitted model parameters coef(object).
Logical. If TRUE the fitted value at each data
point will be computed using a leave-one-out method. See Details.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.
This is a method for the generic function fitted
for the class "lppm" of fitted point process models on a linear
network.
The locations \(u\) at which the fitted conditional intensity/trend
is evaluated, are the points of the
quadrature scheme used to fit the model in ppm.
They include the data points (the points of the original point pattern
dataset x) and other ``dummy'' points
in the window of observation.
If leaveoneout=TRUE, fitted values will be computed
for the data points only, using a ‘leave-one-out’ rule:
the fitted value at X[i] is effectively computed by
deleting this point from the data and re-fitting the model to the
reduced pattern X[-i], then predicting the value at
X[i]. (Instead of literally performing this calculation,
we apply a Taylor approximation using the influence function
computed in dfbetas.ppm.
lppm,
predict.lppm
fit <- lppm(spiders~x+y)
a <- fitted(fit)
b <- fitted(fit, dataonly=TRUE)
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