StraussHard             package:spatstat             R Documentation

_T_h_e _S_t_r_a_u_s_s / _H_a_r_d _C_o_r_e _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 ``Strauss/ hard core'' point process
     model which can then be fitted to point pattern data.

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

       StraussHard(r, hc)

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

       r: The interaction radius of the Strauss interaction

      hc: The hard core distance

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

     A Strauss/hard core process with interaction radius r, hard core
     distance h < r, and  parameters beta and gamma, is a pairwise
     interaction point process in which

        *  distinct points are not allowed to come closer than a
           distance h apart

        *  each pair of points closer than r units apart contributes a
           factor gamma to the probability density.

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

     An object of class `"interact"' describing the interpoint
     interaction structure of the ``Strauss/hard core'' process with
     Strauss interaction radius r and hard core distance `hc'.

_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>

_R_e_f_e_r_e_n_c_e_s:

     Baddeley, A. and Turner, R. (2000) Practical maximum
     pseudolikelihood for spatial point patterns. Australian and New
     Zealand Journal of Statistics 42, 283-322.

     Ripley, B.D. (1981)  Spatial statistics. John Wiley and Sons.

     Strauss, D.J. (1975) A model for clustering. Biometrika 63,
     467-475.

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

     `mpl', `pairwise.family', `ppm.object'

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

        library(spatstat)
        StraussHard(r=1,hc=0.02)
        # prints a sensible description of itself

        data(cells) 
        mpl(cells, ~1, StraussHard(r=0.1, hc=0.05), rbord=0.1)
        # fit the stationary Strauss/hard core  process to `cells'

        mpl(cells, ~ polynom(x,y,3), StraussHard(r=0.1, hc=0.05), rbord=0.1)
        # fit a nonstationary Strauss/hard core process
        # with log-cubic polynomial trend


