csr                 package:splancs                 R Documentation

_G_e_n_e_r_a_t_e _c_o_m_p_l_e_t_e_l_y _s_p_a_t_i_a_l_l_y _r_a_n_d_o_m _p_o_i_n_t_s _o_n _a _p_o_l_y_g_o_n

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

     Generate completely spatially random points on a polygon.

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

     csr(poly,npoints)

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

    poly: A polygon data set. 

 npoints: The number of points to generate. 

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

     A point data set consisting of `npoints' points distributed
     randomly, i.e. as an independent random sample from the uniform
     distribution in the polygon defined by `poly'.

_M_E_T_H_O_D:

     `csr' generates points randomly in the bounding box of `poly,'
     then uses  `pip' to extract those in the polygon. If the number of
     points remaining is less than that required, `csr' generates some
     more points in the bounding box until at least `npoints' remain
     inside the polygon. If too many points are generated then the list
     of points is truncated.

_S_i_d_e _E_f_f_e_c_t_s:

     Uses `runif()' to generate random numbers and so updates
     `.Random.seed', the standard S random number generator seed.

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

     Rowlingson, B. and Diggle, P. 1993 Splancs: spatial point pattern
     analysis code in S-Plus.  Computers and Geosciences, 19, 627-655;
     the original sources can be accessed at: <URL:
     http://www.maths.lancs.ac.uk/~rowlings/Splancs/>. See also Bivand,
     R. and Gebhardt, A. 2000 Implementing functions for spatial
     statistical analysis using the R language. Journal of Geographical
     Systems, 2, 307-317.

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

     data(cardiff)
     nsim <- 29
     emp.Ghat <- Ghat(as.points(cardiff), seq(0,30,1))
     av.Ghat <- numeric(length(emp.Ghat))
     U.Ghat <- numeric(length(emp.Ghat))
     L.Ghat <- numeric(length(emp.Ghat))
     U.Ghat <- -99999
     L.Ghat <- 99999
     for(i in 1:nsim) {
     S.Ghat <- Ghat(csr(cardiff$poly, length(cardiff$x)), seq(0,30,1))
     av.Ghat <- av.Ghat + S.Ghat
     L.Ghat <- pmin(S.Ghat, L.Ghat)
     U.Ghat <- pmax(S.Ghat, U.Ghat)
     }
     av.Ghat <- av.Ghat/nsim
     plot(av.Ghat, emp.Ghat, type="l", xlim=c(0,1), ylim=c(0,1), 
     xlab="Simulated average G", ylab="Empirical G")
     lines(c(0,1),c(0,1),lty=2)
     lines(U.Ghat,emp.Ghat,lty=3)
     lines(L.Ghat,emp.Ghat,lty=3)

