Poisson               package:spatstat               R Documentation

_P_o_i_s_s_o_n _P_o_i_n_t _P_r_o_c_e_s_s _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

      Poisson()

_D_e_t_a_i_l_s:

     The function `mpl()', 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.

_V_a_l_u_e:

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

_A_u_t_h_o_r(_s):

     Adrian Baddeley adrian@maths.uwa.edu.au <URL:
     http://www.maths.uwa.edu.au/~adrian/> and Rolf Turner
     rolf@math.unb.ca <URL: http://www.math.unb.ca/~rolf>

_S_e_e _A_l_s_o:

     `Strauss', `StraussHard'

_E_x_a_m_p_l_e_s:

      library(spatstat)

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

      data(longleaf)
      longadult <- longleaf[longleaf$marks >= 30, ]
      longadult <- unmark(longadult)
      mpl(longadult, ~ x, Poisson())
      # fit the nonstationary Poisson process 
      # with intensity lambda(x,y) = exp( a + bx)

      data(lansing)
      # trees marked by species
      mpl(lansing, ~ marks, Poisson())
      # fit stationary marked Poisson process
      # with different intensity for each species


      mpl(lansing, ~ marks * polynom(x,y,3), Poisson())

      # fit nonstationary marked Poisson process
      # with different log-cubic trend for each species

