spatstat.model (version 3.3-5)

Poisson: Poisson Point Process Model

Description

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

Usage

Poisson()

Value

An object of class "interact"

describing the interpoint interaction structure of the Poisson point process (namely, there are no interactions).

Author

Adrian Baddeley , Rolf Turner and Ege Rubak

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.

See Also

ppm, Strauss

Examples

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

 lon <- longleaf
 # \testonly{
   lon <- lon[seq(1, npoints(lon), by=50)]
 # }
 longadult <- unmark(subset(lon, marks >= 30))
 ppm(longadult ~ x, Poisson())
 # fit the nonstationary Poisson process 
 # with intensity lambda(x,y) = exp( a + bx)

 # trees marked by species
 lans <- lansing
 # \testonly{
     lans <- lans[seq(1, npoints(lans), by=30)]
 # }
 ppm(lans ~ marks, Poisson())
 # fit stationary marked Poisson process
 # with different intensity for each species

 # \donttest{
   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
   ppm(amacrine ~ marks * polynom(x,y,2), Poisson())
# }