vario                package:pastecs                R Documentation

_C_o_m_p_u_t_e _a_n_d _p_l_o_t _a _s_e_m_i-_v_a_r_i_o_g_r_a_m

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

     Compute a classical semi-variogram for a single regular time
     series

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

     vario(x, max.dist=length(x)/3, plotit=TRUE, vario.data=NULL)

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

       x: a vector or an univariate time series 

max.dist: the maximum distance to calculate. By default, it is the
          third of the number of observations 

  plotit: If 'plotit=TRUE' then the graph of the semi-variogram is
          plotted 

vario.data: data coming from a previous call to 'vario()'. Call the
          function again with these data to plot the corresponding
          graph 

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

     A data frame containing distance and semi-variogram values

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

     Frdric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean
     (phgrosjean@sciviews.org)

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

     David, M., 1977. _Developments in geomathematics. Tome 2:
     Geostatistical or reserve estimation._ Elsevier Scientific,
     Amsterdam. 364 pp.

     Delhomme, J.P., 1978. _Applications de la thorie des variables
     rgionalises dans les sciences de l'eau._ Bull. BRGM, section 3
     n4:341-375.

     Matheron, G., 1971. _La thorie des variables rgionalises et ses
     applications._ Cahiers du Centre de Morphologie Mathmatique de
     Fontainebleau. Fasc. 5 ENSMP, Paris. 212 pp.

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

     'disto'

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

     data(bnr)
     vario(bnr[, 4])

