khat                 package:splancs                 R Documentation

_K-_f_u_n_c_t_i_o_n

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

     Calculates an estimate of the K-function

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

     khat(pts,poly,s)

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

     pts: A points data set 

    poly: A polygon containing the points 

       s: A vector of distances at which to calculate the K function 

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

     A vector like `s' containing the value of K at the points in `s'.

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

     The K function is defined as the expected number of further points
     within a distance s of an arbitrary point, divided by the overall
     density of the points.  In practice an edge-correction is required
     to avoid biasing the estimation due to non-recording of points
     outside the polygon.

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

     Ripley, B.D. 1976 The second-order analysis of stationary point
     processes, J. Appl. Prob, 13 255-266; 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.

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

     `Kenv.csr'

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

     data(cardiff)
     plot(seq(2,30,2), sqrt(khat(as.points(cardiff), cardiff$poly, 
     seq(2,30,2))/pi)-seq(2,30,2), type="l", xlab="Splancs - polygon boundary", 
     ylab="Estimated L", ylim=c(-1,1.5))

