Given a fitted point process model on a linear network, compute the fitted intensity or conditional intensity of the model.
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The fitted model. An object of class
Type of values to be computed. Either
Optional. Locations at which predictions should be computed. Either a data frame with two columns of coordinates, or a binary image mask.
Optional. Numeric vector of model coefficients,
to be used instead of the fitted coefficients
Optional arguments passed to
This function computes the fitted poin process intensity,
fitted conditional intensity, or standard error of the fitted
intensity, for a point process model on a linear network.
It is a method for the generic
for the class
object should be an object of class
lppm) representing a point process model
on a linear network.
Predicted values are computed at the locations given by the
locations. If this argument is missing,
then predicted values are computed at a fine grid of points
on the linear network.
locations is missing or
NULL (the default),
the return value is a pixel image (object of class
which inherits class
corresponding to a discretisation
of the linear network, with numeric pixel values giving the
predicted values at each location on the linear network.
locations is a data frame, the result is a
numeric vector of predicted values at the locations specified by
the data frame.
locations is a binary mask, the result is a pixel image
with predicted values computed at the pixels of the mask.
A pixel image (object of class
"linim" which inherits
a numeric vector, depending on the argument
Ang, Q.W. (2010) Statistical methodology for events on a network. Master's thesis, School of Mathematics and Statistics, University of Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591–617.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.
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