pcp                 package:splancs                 R Documentation

_F_i_t _a _P_o_i_s_s_o_n _c_l_u_s_t_e_r _p_r_o_c_e_s_s

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

     The function fits a Poisson cluster process to point data for a
     given enclosing polygon and fit parameters

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

     pcp(point.data, poly.data, h0=NULL, expo=0.25, n.int=20)

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

point.data: a points object

poly.data: a polygon enclosing the study region

      h0: upper bound of integration in the criterion function

    expo: exponent in the criterion function

   n.int: number of intervals used to approximate the integral in the
          criterion function with a sum

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

     The function returns an object as returned by `optim', including: 

     par: The best set of parameters s2 and rho found

   value: The value of the fit corresponding to `par'

convergence: `0' indicates successful convergence

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

     Giovanni Petris <GPetris@uark.edu>,  Roger.Bivand@nhh.no

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

     Diggle, P. J. (1983) Statistical analysis of spatial point
     patterns, London: Academic Press, pp. 55-57 and 78-81; Bailey, T.
     C. and Gatrell, A. C. (1995) Interactive spatial data analysis,
     Harlow: Longman, pp. 106-109.

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

     `optim', `pcp.sim', `Kenv.pcp', `khat'

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

     data(cardiff)
     polymap(cardiff$poly)
     pointmap(as.points(cardiff), add=T)
     title("Locations of homes of 168 juvenile offenders")
     pcp.fit <- pcp(as.points(cardiff), cardiff$poly, h0=30, n.int=30)
     pcp.fit

