StraussHard
From spatstat v1.3-4
by Adrian Baddeley
The Strauss / Hard Core Point Process Model
Creates an instance of the ``Strauss/ hard core'' point process model which can then be fitted to point pattern data.
- Keywords
- spatial
Usage
StraussHard(r, hc)
Arguments
- r
- The interaction radius of the Strauss interaction
- hc
- The hard core distance
Details
A Strauss/hard core process with interaction radius $r$, hard core distance $h < r$, and parameters $\beta$ and $\gamma$, is a pairwise interaction point process in which
- distinct points are not allowed to come closer than a distance$h$apart
- each pair of points closer than$r$units apart contributes a factor$\gamma$to the probability density.
Value
- An object of class
"interact"
describing the interpoint interaction structure of the ``Strauss/hard core'' process with Strauss interaction radius $r$ and hard core distancehc
.
eqn
$\log(\gamma)$
deqn
$$f(x_1,\ldots,x_n) = \alpha \beta^{n(x)} \gamma^{s(x)}$$
code
StraussHard()
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.
Strauss, D.J. (1975) A model for clustering. Biometrika 63, 467--475.
See Also
Examples
library(spatstat)
StraussHard(r=1,hc=0.02)
# prints a sensible description of itself
data(cells)
mpl(cells, ~1, StraussHard(r=0.1, hc=0.05), rbord=0.1)
# fit the stationary Strauss/hard core process to `cells'
mpl(cells, ~ polynom(x,y,3), StraussHard(r=0.1, hc=0.05), rbord=0.1)
# fit a nonstationary Strauss/hard core process
# with log-cubic polynomial trend
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