# Hardcore

##### The Hard Core Point Process Model

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

##### Usage

`Hardcore(hc)`

##### Arguments

- hc
- The hard core distance

##### Details

A hard core process with hard core distance $h < r$ and abundance parameter $\beta$ is a pairwise interaction point process in which distinct points are not allowed to come closer than a distance $h$ apart.

The probability density is zero if any pair of points is closer than $h$ units apart, and otherwise equals $$f(x_1,\ldots,x_n) = \alpha \beta^{n(x)}$$ where $x_1,\ldots,x_n$ represent the points of the pattern, $n(x)$ is the number of points in the pattern, and $\alpha$ is the normalising constant.

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 hard core process
pairwise interaction is
yielded by the function `Hardcore()`

. See the examples below.

##### Value

- An object of class
`"interact"`

describing the interpoint interaction structure of the hard core process with hard core distance`hc`

.

##### 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.

##### See Also

`Strauss`

,
`StraussHard`

,
`MultiHard`

,
`ppm`

,
`pairwise.family`

,
`ppm.object`

##### Examples

```
Hardcore(0.02)
# prints a sensible description of itself
data(cells)
ppm(cells, ~1, Hardcore(0.05))
# fit the stationary hard core process to `cells'
ppm(cells, ~ polynom(x,y,3), Hardcore(0.05))
# fit a nonstationary hard core process
# with log-cubic polynomial trend
```

*Documentation reproduced from package spatstat, version 1.31-2, License: GPL (>= 2)*