DiggleGatesStibbard {spatstat.model} | R Documentation |
Diggle-Gates-Stibbard Point Process Model
Description
Creates an instance of the Diggle-Gates-Stibbard point process model which can then be fitted to point pattern data.
Usage
DiggleGatesStibbard(rho)
Arguments
rho |
Interaction range |
Details
Diggle, Gates and Stibbard (1987) proposed a pairwise interaction
point process in which each pair of points separated by
a distance d
contributes a factor e(d)
to the
probability density, where
e(d) = \sin^2\left(\frac{\pi d}{2\rho}\right)
for d < \rho
, and e(d)
is equal to 1
for d \ge \rho
.
The function ppm()
, which fits point process models to
point pattern data, requires an argument
of class "interact"
describing the interpoint interaction
structure of the model to be fitted.
The appropriate description of the Diggle-Gates-Stibbard
pairwise interaction is
yielded by the function DiggleGatesStibbard()
.
See the examples below.
Note that this model does not have any regular parameters
(as explained in the section on Interaction Parameters
in the help file for ppm
).
The parameter \rho
is not estimated by ppm
.
Value
An object of class "interact"
describing the interpoint interaction
structure of the Diggle-Gates-Stibbard
process with interaction range rho
.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk
References
Baddeley, A. and Turner, R. (2000) Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42, 283–322.
Ripley, B.D. (1981) Spatial statistics. John Wiley and Sons.
Diggle, P.J., Gates, D.J., and Stibbard, A. (1987) A nonparametric estimator for pairwise-interaction point processes. Biometrika 74, 763 – 770. Scandinavian Journal of Statistics 21, 359–373.
See Also
ppm
,
pairwise.family
,
DiggleGratton
,
rDGS
,
ppm.object
Examples
DiggleGatesStibbard(0.02)
# prints a sensible description of itself
ppm(cells ~1, DiggleGatesStibbard(0.05))
# fit the stationary D-G-S process to `cells'
ppm(cells ~ polynom(x,y,3), DiggleGatesStibbard(0.05))
# fit a nonstationary D-G-S process
# with log-cubic polynomial trend