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 pattern, or at the points of the quadrature scheme used to fit the model.
## S3 method for class 'ppm': fitted(object, \dots, type="lambda", dataonly=FALSE, check=TRUE, repair=TRUE)
- The fitted point process model (an object of class
- String (partially matched) indicating whether the fitted value is the
conditional intensity (
"lambda") or the trend (
- Logical. If
TRUE, then values will only be computed at the points of the data point pattern. If
FALSE, then values will be computed at all the points of the quadrature scheme used to fit the model, including the point
- Logical value indicating whether to check the internal format
object. If there is any possibility that this object has been restored from a dump file, or has otherwise lost track of the environment where it was originally compu
- Logical value indicating whether to repair the internal format
object, if it is found to be damaged.
object must be a fitted point process model
(object of class
"ppm"). Such objects are produced by the
This function evaluates the conditional intensity
or spatial trend $\hat b(u)$ 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/trend
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
type="trend") the fitted spatial trend. Entries in this vector correspond to the quadrature points (data or dummy points) used to fit the model. The quadrature points can be extracted from
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes (with discussion). Journal of the Royal Statistical Society, Series B 67, 617--666.
data(cells) str <- ppm(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)