precompute         package:spatialCovariance         R Documentation

_P_r_e_c_o_m_p_u_t_e _S_t_e_p _f_o_r _C_o_m_p_u_t_i_n_g _C_o_v_a_r_i_a_n_c_e _M_a_t_r_i_x

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

     For a lattice with 'nr' rows and 'nc' columns on only needs to
     compute $n=nr\times nc$ entries to fill the whole covariance
     matrix (of size $n \times n$).  For example, the diagonal entries
     will all be the same so one only needs to compute it once and know
     that the value needs to be placed along the diagonal. This
     algorithm figures out which entries need to be computed, and how
     to insert them into the covariance matrix.

     When an anisotropy term 'aniso' is included in the direction of
     rows and columns it changes how distance is measure from
     $\sqrt{x^2+y^2}$ to $\sqrt{x^2+alpha^2 y^2}$.  This amounts to
     stretching the lattice in the appropriate direction by a factor of
     $alpha$.  We can update the results of the 'precompute' stage very
     easily.

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

     info <- precompute(nrows,ncols,rowwidth,colwidth,rowsep,colsep,cat.level)
     info <- precompute.update(info,aniso=2)  ## geometric anisotropy update

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

nrows,ncols: Number of rows and columns in the lattice

rowwidth, colwidth: Dimensions of the rectangle

rowsep,colsep: Vectors of separations between rows and columns. Pass
          scalars if the separations are constant in each direction.

cat.level: 0,0.5,1, changes the amount of output.  Output is limited to
          times for various stages of the computation

   aniso: Value of anisotropy parameter in the direction of rows and
          columns.  Should be a positive number.

    info: Result of the precompute stage

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

     David Clifford

