Poisson
From spatstat v1.6-11
by Adrian Baddeley
Poisson Point Process Model
Creates an instance of the Poisson point process model which can then be fitted to point pattern data.
- Keywords
- 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
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>
Community examples
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