Creates an instance of the hard core point process model which can then be fitted to point pattern data.
The hard core distance
A hard core process with hard core distance h 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,…,x_n) = alpha . beta^n(x)
where x,…,x[n] represent the points of the pattern, n(x) is the number of points in the pattern, and alpha is the normalising constant.
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 hard core process
pairwise interaction is
yielded by the function
Hardcore(). See the examples below.
If the hard core distance argument
hc is missing or
it will be estimated from the data when
ppm is called.
The estimated value of
hc is the minimum nearest neighbour distance
multiplied by n/(n+1), where n is the
number of data points.
An object of class
describing the interpoint interaction
structure of the hard core
process with hard core distance
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Hardcore(0.02) # prints a sensible description of itself data(cells) ## Not run: ppm(cells, ~1, Hardcore(0.05)) # fit the stationary hard core process to `cells' ## End(Not run) ppm(cells, ~1, Hardcore()) ppm(cells, ~ polynom(x,y,3), Hardcore(0.05)) # fit a nonstationary hard core process # with log-cubic polynomial trend
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.