Fitted Conditional Intensity for Point Process Model
Given a point process model fitted to a point pattern, compute the fitted conditional intensity of the model at the points of the quadrature scheme used to fit the model.
- The fitted point process model (an object of class
object must be a fitted point process model
(object of class
"ppm"). Such objects are produced by the maximum
pseudolikelihood fitting algorithm
This function evaluates the conditional intensity
$\hat\lambda(u, x)$ of the fitted point process
model for certain locations $u$,
x is the original point pattern dataset to which
the model was fitted.
The locations $u$ at which the fitted conditional intensity
is evaluated, are the points of the
quadrature scheme used to fit the model in
They include the data points (the points of the original point pattern
x) and other ``dummy'' points
in the window of observation.
predict.ppm to compute the fitted conditional
intensity at other locations or with other values of the
- A vector containing the values of the fitted conditional intensity.
Entries in this vector correspond to the quadrature points (data or
dummy points) used to fit the model. The quadrature points can be
Baddeley, A., Moller, J. and Turner, R. (2004) Residuals for spatial point processes. In preparation.
require(spatstat) data(cells) str <- mpl(cells, ~x, Strauss(r=0.15), rbord=0.15) lambda <- fitted(str) # extract quadrature points in corresponding order quadpoints <- union.quad(quad.ppm(str)) # plot conditional intensity values # as circles centred on the quadrature points quadmarked <- setmarks(quadpoints, lambda) plot(quadmarked)