Creates an instance of a saturated pairwise interaction point process model with piecewise constant potential function. The model can then be fitted to point pattern data.
vector of jump points for the potential function
vector of saturation values, or a single saturation value
This is a generalisation of the Geyer saturation point process model,
Geyer, to the case of multiple interaction
distances. It can also be described as the saturated analogue of a
pairwise interaction process with piecewise-constant pair potential,
The saturated point process with interaction radii r, ..., r[k], saturation thresholds s,...,s[k], intensity parameter beta and interaction parameters gamma, ..., gamma[k], is the point process in which each point x[i] in the pattern X contributes a factor
beta gamma^v(1, x_i, X) ... gamma[k]^v(k, x_i, X)
to the probability density of the point pattern, where
v(j, x_i, X) = min(s[j], t(j, x_i, X))
where t(j,x[i],X) denotes the number of points in the pattern X which lie at a distance between r[j-1] and r[j] from the point x[i]. We take r = 0 so that t(1, x[i], X) is the number of points of X that lie within a distance r of the point x[i].
SatPiece is used to fit this model to data.
ppm(), which fits point process models to
point pattern data, requires an argument
"interact" describing the interpoint interaction
structure of the model to be fitted.
The appropriate description of the piecewise constant Saturated pairwise
interaction is yielded by the function
See the examples below.
Simulation of this point process model is not yet implemented. This model is not locally stable (the conditional intensity is unbounded).
r specifies the vector of interaction distances.
The entries of
r must be strictly increasing, positive numbers.
sat specifies the vector of saturation parameters.
It should be a vector of the same length as
r, and its entries
should be nonnegative numbers. Thus
sat corresponds to the
distance range from
sat to the
distance range from
sat may be a single number, and this saturation
value will be applied to every distance range.
Infinite values of the
saturation parameters are also permitted; in this case
v(j, x_i, X) = t(j, x_i, X)
and there is effectively no ‘saturation’ for the distance range in
question. If all the saturation parameters are set to
the model is effectively a pairwise interaction process, equivalent to
PairPiece (however the interaction parameters
gamma obtained from
SatPiece are the
square roots of the parameters gamma
r is a single number, this model is virtually equivalent to the
Geyer process, see
An object of class
describing the interpoint interaction
structure of a point process.
in collaboration with Hao Wang and Jeff Picka
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SatPiece(c(0.1,0.2), c(1,1)) # prints a sensible description of itself SatPiece(c(0.1,0.2), 1) data(cells) ppm(cells, ~1, SatPiece(c(0.07, 0.1, 0.13), 2)) # fit a stationary piecewise constant Saturated pairwise interaction process ## Not run: ppm(cells, ~polynom(x,y,3), SatPiece(c(0.07, 0.1, 0.13), 2)) # nonstationary process with log-cubic polynomial trend ## End(Not run)