predict.lppm
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.
Usage
"predict"(object, ..., type = "trend", locations = NULL, new.coef=NULL)
Arguments
 object

The fitted model. An object of class
"lppm"
, seelppm
.  type

Type of values to be computed. Either
"trend"
,"cif"
or"se"
.  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
coef(object)
when calculating the prediction.  ...

Optional arguments passed to
as.mask
to determine the pixel resolution (iflocations
is missing).
Details
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 orNULL
(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.
Value

A pixel image (object of class
"linim"
which inherits
class "im"
) or
a numeric vector, depending on the argument locations
.
See Details.
References
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 secondorder analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591617.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.
See Also
Examples
X < runiflpp(12, simplenet)
fit < lppm(X ~ x)
v < predict(fit, type="trend")
plot(v)