gcv                  package:locfit                  R Documentation

_C_o_m_p_u_t_e _g_e_n_e_r_a_l_i_z_e_d _c_r_o_s_s-_v_a_l_i_d_a_t_i_o_n _s_t_a_t_i_s_t_i_c.

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

     The calling sequence for 'gcv' matches those for the 'locfit' or
     'locfit.raw' functions. The fit is not returned; instead, the
     returned object contains Wahba's generalized cross-validation
     score for the fit.

     The GCV score is exact (up to numerical roundoff) if the
     'ev="data"' argument is provided. Otherwise, the residual
     sum-of-squares and degrees of freedom are computed using locfit's
     standard interpolation based approximations.

     For likelihood models, GCV is computed uses the deviance in place
     of the residual sum of squares. This produces useful results but I
     do not know of any theory validating this extension.

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

     gcv(x, ...)

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

       x: either a numeric vector or a data frame (same as the
          arguments to 'locfit').

     ...: other argument to 'locfit'.

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

     'locfit', 'locfit.raw', 'gcvplot'

