fitted.ppm {spatstat} | R Documentation |
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, ..., type="lambda", dataonly=FALSE, drop=FALSE, check=TRUE, repair=TRUE)
object |
The fitted point process model (an object of class |
... |
Ignored. |
type |
String (partially matched) indicating whether the fitted value is the
conditional intensity ( |
dataonly |
Logical. If |
drop |
Logical value determining whether to delete quadrature points that were not used to fit the model. |
check |
Logical value indicating whether to check the internal format
of |
repair |
Logical value indicating whether to repair the internal format
of |
The argument object
must be a fitted point process model
(object of class "ppm"
). Such objects are produced by the
model-fitting algorithm ppm
).
This function evaluates the conditional intensity
lambdahat(u,x)
or spatial trend bhat(u) of the fitted point process
model for certain locations u,
where 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 ppm
.
They include the data points (the points of the original point pattern
dataset x
) and other “dummy” points
in the window of observation.
The argument drop
is explained in quad.ppm
.
Use predict.ppm
to compute the fitted conditional
intensity at other locations or with other values of the
explanatory variables.
A vector containing the values of the fitted conditional intensity
or (if 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 object
by union.quad(quad.ppm(object))
.
Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
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)) 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)