corSpatial               package:nlme               R Documentation

_S_p_a_t_i_a_l _C_o_r_r_e_l_a_t_i_o_n _S_t_r_u_c_t_u_r_e

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

     This function is a constructor for the `corSpatial' class,
     representing a spatial correlation structure. This class is
     "virtual", having four "real" classes, corresponding to specific
     spatial correlation structures, associated with it: `corExp',
     `corGaus', `corLin', `corRatio', and `corSpher'. The returned
     object will inherit from one of these "real" classes, determined
     by the `type' argument, and from the "virtual" `corSpatial' class.
     Objects created using this constructor must later be initialized
     using the appropriate `initialize' method.

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

     corSpatial(value, form, nugget, type, metric, fixed)

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

   value: an optional vector with the parameter values in constrained
          form. If `nugget' is `FALSE', `value' can have only one
          element, corresponding to the "range" of the spatial
          correlation structure, which must be greater than zero. If
          `nugget' is `TRUE', meaning that a nugget effect is present,
          `value' can contain one or two elements, the first being the
          "range" and the second the "nugget effect" (one minus the
          correlation between two observations taken arbitrarily close
          together); the first must be greater than zero and the second
          must be between zero and one. Defaults to `numeric(0)', which
          results in a range of 90% of the minimum distance and a
          nugget effect of 0.1 being assigned to the parameters when
          `object' is initialized.

    form: a one sided formula of the form `~ S1+...+Sp', or `~
          S1+...+Sp | g', specifying spatial covariates `S1' through
          `Sp' and,  optionally, a grouping factor `g'.  When a
          grouping factor is present in `form', the correlation
          structure is assumed to apply only to observations within the
          same grouping level; observations with different grouping
          levels are assumed to be uncorrelated. Defaults to `~ 1',
          which corresponds to using the order of the observations in
          the data as a covariate, and no groups.

  nugget: an optional logical value indicating whether a nugget effect
          is present. Defaults to `FALSE'.

    type: an optional character string specifying the desired type of
          correlation structure. Available types include `"spherical"',
          `"exponential"', `"gaussian"', `"linear"', and `"rational"'.
          See the documentation on the functions `corSpher', `corExp',
          `corGaus', `corLin', and `corRatio' for a description of
          these correlation structures. Partial matching of arguments
          is used, so only the first character needs to be
          provided.Defaults to `"spherical"'.

  metric: an optional character string specifying the distance metric
          to be used. The currently available options are `"euclidean"'
          for the root sum-of-squares of distances; `"maximum"' for the
          maximum difference; and `"manhattan"' for the sum of the
          absolute differences. Partial matching of arguments is used,
          so only the first three characters need to be provided.
          Defaults to `"euclidean"'.

   fixed: an optional logical value indicating whether the coefficients
          should be allowed to vary in the optimization, or kept fixed
          at their initial value. Defaults to `FALSE', in which case
          the coefficients are allowed to vary.

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

     an object of class determined by the `type' argument and also
     inheriting from class `corSpatial', representing a spatial
     correlation structure.

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

     Jose Pinheiro and Douglas Bates

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

     Cressie, N.A.C. (1993), "Statistics for Spatial Data", J. Wiley &
     Sons. Venables, W.N. and Ripley, B.D. (1997) "Modern Applied
     Statistics with S-plus", 2nd Edition, Springer-Verlag. Littel,
     Milliken, Stroup, and Wolfinger (1996) "SAS Systems for Mixed
     Models", SAS Institute.

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

     `corExp', `corGaus', `corLin', `corRatio', `corSpher',
     `initialize.corStruct', `dist'

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

     sp1 <- corSpatial(form = ~ x + y + z, type = "g", metric = "man")

