# Softcore

##### The Soft Core Point Process Model

Creates an instance of the Soft Core point process model which can then be fitted to point pattern data.

##### Usage

`Softcore(kappa, sigma0=NA)`

##### Arguments

- kappa
The exponent \(\kappa\) of the Soft Core interaction

- sigma0
Optional. Initial estimate of the parameter \(\sigma\). A positive number.

##### Details

The (stationary) Soft Core point process with parameters \(\beta\) and \(\sigma\) and exponent \(\kappa\) is the pairwise interaction point process in which each point contributes a factor \(\beta\) to the probability density of the point pattern, and each pair of points contributes a factor $$ \exp \left\{ - \left( \frac{\sigma}{d} \right)^{2/\kappa} \right\} $$ to the density, where \(d\) is the distance between the two points.

Thus the process has probability density $$ f(x_1,\ldots,x_n) = \alpha \beta^{n(x)} \exp \left\{ - \sum_{i < j} \left( \frac{\sigma}{||x_i-x_j||} \right)^{2/\kappa} \right\} $$ where \(x_1,\ldots,x_n\) represent the points of the pattern, \(n(x)\) is the number of points in the pattern, \(\alpha\) is the normalising constant, and the sum on the right hand side is over all unordered pairs of points of the pattern.

This model describes an ``ordered'' or ``inhibitive'' process, with the interpoint interaction decreasing smoothly with distance. The strength of interaction is controlled by the parameter \(\sigma\), a positive real number, with larger values corresponding to stronger interaction; and by the exponent \(\kappa\) in the range \((0,1)\), with larger values corresponding to weaker interaction. If \(\sigma = 0\) the model reduces to the Poisson point process. If \(\sigma > 0\), the process is well-defined only for \(\kappa\) in \((0,1)\). The limit of the model as \(\kappa \to 0\) is the hard core process with hard core distance \(h=\sigma\).

The nonstationary Soft Core process is similar except that the contribution of each individual point \(x_i\) is a function \(\beta(x_i)\) of location, rather than a constant beta.

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 Soft Core process pairwise interaction is
yielded by the function `Softcore()`

. See the examples below.

The main argument is the exponent `kappa`

.
When `kappa`

is fixed, the model becomes an exponential family
with canonical parameters \(\log \beta\)
and $$
\log \gamma = \frac{2}{\kappa} \log\sigma
$$
The canonical parameters are estimated by `ppm()`

, not fixed in
`Softcore()`

.

The optional argument `sigma0`

can be used to improve
numerical stability. If `sigma0`

is given, it should be a positive
number, and it should be a rough estimate of the
parameter \(\sigma\).

##### Value

An object of class `"interact"`

describing the interpoint interaction
structure of the Soft Core process with exponent \(\kappa\).

##### References

Ogata, Y, and Tanemura, M. (1981).
Estimation of interaction potentials of spatial point patterns
through the maximum likelihood procedure.
*Annals of the Institute of Statistical Mathematics*, B
**33**, 315--338.

Ogata, Y, and Tanemura, M. (1984).
Likelihood analysis of spatial point patterns.
*Journal of the Royal Statistical Society, series B*
**46**, 496--518.

##### See Also

##### Examples

```
# NOT RUN {
data(cells)
ppm(cells, ~1, Softcore(kappa=0.5), correction="isotropic")
# fit the stationary Soft Core process to `cells'
# }
```

*Documentation reproduced from package spatstat, version 1.52-1, License: GPL (>= 2)*