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

`Strauss(r)`

r

The interaction radius of the Strauss process

An object of class `"interact"`

describing the interpoint interaction
structure of the Strauss process with interaction radius \(r\).

The (stationary) Strauss process with interaction radius \(r\) and parameters \(\beta\) and \(\gamma\) 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 closer than \(r\) units apart contributes a factor \(\gamma\) to the density.

Thus the probability density is $$ f(x_1,\ldots,x_n) = \alpha \beta^{n(x)} \gamma^{s(x)} $$ where \(x_1,\ldots,x_n\) represent the points of the pattern, \(n(x)\) is the number of points in the pattern, \(s(x)\) is the number of distinct unordered pairs of points that are closer than \(r\) units apart, and \(\alpha\) is the normalising constant.

The interaction parameter \(\gamma\) must be less than or equal to \(1\) so that this model describes an ``ordered'' or ``inhibitive'' pattern.

The nonstationary Strauss 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 Strauss process pairwise interaction is
yielded by the function `Strauss()`

. See the examples below.

Note the only argument is the interaction radius `r`

.
When `r`

is fixed, the model becomes an exponential family.
The canonical parameters \(\log(\beta)\)
and \(\log(\gamma)\)
are estimated by `ppm()`

, not fixed in
`Strauss()`

.

Kelly, F.P. and Ripley, B.D. (1976)
On Strauss's model for clustering.
*Biometrika* **63**, 357--360.

Strauss, D.J. (1975)
A model for clustering.
*Biometrika* **62**, 467--475.

# NOT RUN { Strauss(r=0.1) # prints a sensible description of itself # ppm(cells ~1, Strauss(r=0.07)) # fit the stationary Strauss process to `cells' ppm(cells ~polynom(x,y,3), Strauss(r=0.07)) # fit a nonstationary Strauss process with log-cubic polynomial trend # }