Predict Point Process Model on Linear Network
Given a fitted point process model on a linear network, compute the fitted intensity or conditional intensity of the model.
## S3 method for class 'lppm'
predict(object, ...,
type = "trend", locations = NULL, new.coef=NULL)object |
The fitted model. An object of class |
type |
Type of values to be computed. Either |
locations |
Optional. Locations at which predictions should be computed. Either a data frame with two columns of coordinates, or a binary image mask. |
new.coef |
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 predict
for the class "lppm".
The argument object should be an object of class "lppm"
(produced by lppm) representing a point process model
on a linear network.
Predicted values are computed at the locations given by the
argument locations. If this argument is missing,
then predicted values are computed at a fine grid of points
on the linear network.
If locations is missing or NULL (the default),
the return value is a pixel image (object of class "linim"
which inherits class "im")
corresponding to a discretisation
of the linear network, with numeric pixel values giving the
predicted values at each location on the linear network.
If locations is a data frame, the result is a
numeric vector of predicted values at the locations specified by
the data frame.
If 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
class "im") or
a numeric vector, depending on the argument locations.
See Details.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
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.
X <- runiflpp(12, simplenet) fit <- lppm(X ~ x) v <- predict(fit, type="trend") plot(v)
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