rNeymanScott            package:spatstat            R Documentation

_S_i_m_u_l_a_t_e _N_e_y_m_a_n-_S_c_o_t_t _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 Neyman-Scott cluster
     process.

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

      rNeymanScott(lambda, rmax, rcluster, 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. 

    rmax: Maximum radius of a random cluster. 

rcluster: A function which generates random clusters. 

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

     ...: Arguments passed to `rcluster' 

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

     This algorithm generates a realisation of the general Neyman-Scott
     process, with the cluster mechanism given by the function
     `rcluster'.  The clusters must have a finite maximum possible
     radius `rmax'.

     We 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, created by calling the
     function `rcluster'.

     The function `rcluster' should expect to be called as
     `rcluster(xp[i],yp[i],...)' for each parent point at a location
     `(xp[i],yp[i])'. The return value of `rcluster' should be a list
     with elements `x,y' which are vectors of equal length giving the
     absolute x and `y' coordinates of the points in the cluster.

_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', `rMatClust'

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

       library(spatstat)
       nclust <-  function(x0, y0, radius, n) {
                                return(runifdisc(n, radius, x0, y0))
                              }
       X <- rNeymanScott(10, 0.2, nclust, radius=0.2, n=5)

