rThomas               package:spatstat               R Documentation

_S_i_m_u_l_a_t_e _T_h_o_m_a_s _P_r_o_c_e_s_s

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

     Generate a random point pattern using the Thomas cluster process.

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

      rThomas(lambda, sigma, mu, win = owin(c(0,1),c(0,1)))

_A_r_g_u_m_e_n_t_s:

  lambda: Intensity of the Poisson process of cluster centres. A single
          positive number. 

   sigma: Standard deviation of displacement of a point from its
          cluster centre. 

      mu: Expected number of points per cluster. 

     win: Window in which to simulate the pattern. An object of class
          `"owin"' or something acceptable to `as.owin'. 

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

     This algorithm generates a realisation of the Thomas process, a
     special case of the Neyman-Scott process.

     The algorithm  generates a uniform Poisson point process of
     ``parent'' points  with intensity `lambda'. Then each parent point
     is replaced by a random cluster of points, the number of points
     per cluster being Poisson (`mu') distributed, and their positions
     being isotropic Gaussian displacements from the cluster parent
     location.

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

     The simulated point pattern (an object of class `"ppp"').

_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:

     `rpoispp', `rNeymanScott'

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

       library(spatstat)
       X <- rThomas(10, 0.2, 5)

