DiggleGratton {spatstat.model} | R Documentation |
Diggle-Gratton model
Description
Creates an instance of the Diggle-Gratton pairwise interaction point process model, which can then be fitted to point pattern data.
Usage
DiggleGratton(delta=NA, rho)
Arguments
delta |
lower threshold |
rho |
upper threshold |
Details
Diggle and Gratton (1984, pages 208-210)
introduced the pairwise interaction point
process with pair potential h(t)
of the form
h(t) = \left( \frac{t-\delta}{\rho-\delta} \right)^\kappa
\quad\quad \mbox{ if } \delta \le t \le \rho
with h(t) = 0
for t < \delta
and h(t) = 1
for t > \rho
.
Here \delta
, \rho
and \kappa
are parameters.
Note that we use the symbol \kappa
where Diggle and Gratton (1984) and Diggle, Gates and Stibbard (1987)
use \beta
, since in spatstat we reserve the symbol
\beta
for an intensity parameter.
The parameters must all be nonnegative,
and must satisfy \delta \le \rho
.
The potential is inhibitory, i.e.\ this model is only appropriate for
regular point patterns. The strength of inhibition increases with
\kappa
. For \kappa=0
the model is
a hard core process with hard core radius \delta
.
For \kappa=\infty
the model is a hard core
process with hard core radius \rho
.
The irregular parameters
\delta, \rho
must be given in the call to
DiggleGratton
, while the
regular parameter \kappa
will be estimated.
If the lower threshold delta
is missing or NA
,
it will be estimated from the data when ppm
is called.
The estimated value of delta
is the minimum nearest neighbour distance
multiplied by n/(n+1)
, where n
is the
number of data points.
Value
An object of class "interact"
describing the interpoint interaction
structure of a point process.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk
References
Diggle, P.J., Gates, D.J. and Stibbard, A. (1987) A nonparametric estimator for pairwise-interaction point processes. Biometrika 74, 763 – 770.
Diggle, P.J. and Gratton, R.J. (1984) Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 – 212.
See Also
Examples
ppm(cells ~1, DiggleGratton(0.05, 0.1))