Poisson

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Percentile

Poisson Point Process Model

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

Keywords
models, spatial
Usage
Poisson()
Details

The function ppm, which fits point process models to point pattern data, requires an argument interaction of class "interact" describing the interpoint interaction structure of the model to be fitted. The appropriate description of the Poisson process is provided by the value of the function Poisson.

This works for all types of Poisson processes including multitype and nonstationary Poisson processes.

Value

  • An object of class "interact" describing the interpoint interaction structure of the Poisson point process (namely, there are no interactions).

See Also

ppm, Strauss

Aliases
  • Poisson
Examples
data(nztrees)
 ppm(nztrees, ~1, Poisson())
 # fit the stationary Poisson process to 'nztrees'
 # no edge correction needed

 data(longleaf)
 <testonly>longleaf <- longleaf[seq(1, longleaf$n, by=50)]</testonly>
 longadult <- longleaf[longleaf$marks >= 30, ]
 longadult <- unmark(longadult)
 ppm(longadult, ~ x, Poisson())
 # fit the nonstationary Poisson process 
 # with intensity lambda(x,y) = exp( a + bx)

 data(lansing)
 # trees marked by species
 <testonly>lansing <- lansing[seq(1,lansing$n, by=30), ]</testonly>
 ppm(lansing, ~ marks, Poisson())
 # fit stationary marked Poisson process
 # with different intensity for each species

ppm(lansing, ~ marks * polynom(x,y,3), Poisson())
 # fit nonstationary marked Poisson process
 # with different log-cubic trend for each species
<testonly># equivalent functionality - smaller dataset
 data(betacells)
 ppm(betacells, ~ marks * polynom(x,y,2), Poisson())</testonly>
Documentation reproduced from package spatstat, version 1.25-1, License: GPL (>= 2)

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