semat                package:spatial                R Documentation

_E_v_a_l_u_a_t_e _K_r_i_g_i_n_g _S_t_a_n_d_a_r_d _E_r_r_o_r _o_f _P_r_e_d_i_c_t_i_o_n _o_v_e_r _a _G_r_i_d

_D_e_s_c_r_i_p_t_i_o_n:

     Evaluate Kriging standard error of prediction over a grid.

_U_s_a_g_e:

     semat(obj, xl, xu, yl, yu, n, se)

_A_r_g_u_m_e_n_t_s:

     obj: object returned by 'surf.gls' 

      xl: limits of the rectangle for grid 

      xu: 

      yl: 

      yu: 

       n: use 'n'  x  'n' grid within the rectangle 

      se: standard error at distance zero as a multiple of the supplied
          covariance. Otherwise estimated, and it assumed that a
          correlation function was supplied. 

_V_a_l_u_e:

     list with components x, y and z suitable for 'contour' and
     'image'.

_R_e_f_e_r_e_n_c_e_s:

     Ripley, B. D. (1981) _Spatial Statistics._ Wiley.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'surf.gls', 'trmat', 'prmat'

_E_x_a_m_p_l_e_s:

     data(topo, package="MASS")
     topo.kr <- surf.gls(2, expcov, topo, d=0.7)
     prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
     contour(prsurf, levels=seq(700, 925, 25))
     sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
     contour(sesurf, levels=c(22,25))