ossfim                 package:gstat                 R Documentation

_K_r_i_g_i_n_g _s_t_a_n_d_a_r_d _e_r_r_o_r_s _a_s _f_u_n_c_t_i_o_n _o_f _g_r_i_d _s_p_a_c_i_n_g _a_n_d _b_l_o_c_k _s_i_z_e

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

     Calculate, for a given variogram model, ordinary block kriging
     standard errors as a function of sampling spaces and block sizes

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

     ossfim(spacings = 1:5, block.sizes = 1:5, model, nmax = 25, debug = 0)

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

spacings: range of grid (data) spacings to be used

block.sizes: range of block sizes to be used

   model: variogram model, output of 'vgm'

    nmax: set the kriging neighbourhood size

   debug: debug level; set to 32 to see a lot of output

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

     data frame with columns 'spacing' (the grid spacing), 'block.size'
     (the block size), and 'kriging.se' (block kriging standard error)

_N_o_t_e:

     The idea is old, simple, but still of value. If you want to map a
     variable with a given accuracy, you will have to sample it.
     Suppose the variogram of the variable is known. Given a regular
     sampling scheme, the kriging standard error decreases when either
     (i) the data spacing is smaller, or (ii) predictions are made for
     larger blocks. This function helps quantifying this relationship.
     Ossfim probably refers to ``optimal sampling scheme for isarithmic
     mapping''.

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

     Edzer J. Pebesma

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

     Burrough, P.A., R.A. McDonnell (1999) Principles of Geographical
     Information Systems. Oxford University Press (e.g., figure 10.11
     on page 261)

     Burgess, T.M., R. Webster, A.B. McBratney (1981) Optimal
     interpolation and isarithmic mapping of soil properties. IV
     Sampling strategy.  The journal of soil science 32(4), 643-660.

     McBratney, A.B., R. Webster (1981) The design of optimal sampling
     schemes for local estimation and mapping of regionalized
     variables: 2 program and examples. Computers and Geosciences 7:
     335-365.

     read more on a simplified, web-based version on <URL:
     http://www.gstat.org/ossfim.html>

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

     krige

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

     x <- ossfim(1:15,1:15, model = vgm(1,"Exp",15))
     levelplot(kriging.se~spacing+block.size, x, 
       main = "Ossfim results, variogram 1 Exp(15)")
     # if you wonder about the decrease in the upper left corner of the graph,
     # try the above with nmax set to 100, or perhaps 200.

