spatstat (version 1.59-0)

predict.lppm: Predict Point Process Model on Linear Network

Description

Given a fitted point process model on a linear network, compute the fitted intensity or conditional intensity of the model.

Usage

# S3 method for lppm
predict(object, ...,
                       type = "trend", locations = NULL, new.coef=NULL)

Arguments

object

The fitted model. An object of class "lppm", see lppm.

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 (if locations is missing).

Value

A pixel image (object of class "linim" which inherits class "im") or a numeric vector, depending on the argument locations. See Details.

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 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.

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 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.

See Also

lpp, linim

Examples

# NOT RUN {
  X <- runiflpp(12, simplenet)
  fit <- lppm(X ~ x)
  v <- predict(fit, type="trend")
  plot(v)
# }