cv.cggd                 package:cggd                 R Documentation

_C_o_m_p_u_t_e_s _K-_f_o_l_d _c_r_o_s_s-_v_a_l_i_d_a_t_e_d _e_r_r_o_r _c_u_r_v_e _f_o_r _c_g_g_d

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the K-fold cross-validated mean squared prediction error
     for cggd.

_U_s_a_g_e:

     cv.cggd(x, y, nfolds = 6, kmax=40 , 
                trace = FALSE, plot.it = TRUE, se=TRUE, ...)

_A_r_g_u_m_e_n_t_s:

       x: Input to cggd

       y: Input to cggd

  nfolds: Number of folds

    kmax: Max number of iterations per model

   trace: Show computations

 plot.it: Plot it

      se: Include standard error bands

     ...: Additional arguments to 'cggd'

_V_a_l_u_e:

      cv: The CV loss curve at each value of k

cv.error: The standard error of the CV curve

_A_u_t_h_o_r(_s):

     Cun-Hui Zhang and Ofer Melnik

_R_e_f_e_r_e_n_c_e_s:

     Cun-Hui Zhang (2007) "Continuous Generalized Gradient Descent"
     _Journal of Computational and Graphical Statistics_ ; see also
     <URL: http://stat.rutgers.edu/~cunhui/software/CGGD.html>.

_E_x_a_m_p_l_e_s:

     data(Wine)
     attach(Wine)
     cv.cggd(x.learning,y.learning,kmax=40,fctr=1e3)
     detach(Wine)

