cggd                  package:cggd                  R Documentation

_F_i_t_s _C_o_n_t_i_n_u_o_u_s _G_e_n_e_r_a_l_i_z_e_d _G_r_a_d_i_e_n_t _D_e_s_c_e_n_t _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     These CGGD models all provide the entire sequence of coefficients
     and fits, to the least squares fit. This package is based on the
     lars package.

_U_s_a_g_e:

     cggd(x, y, beta0 = rep(0,2), kmax = 300, 
                           TRR=FALSE, t0 = 1, TRACE=FALSE,
                           alpha1 = 0, alpha2 = 0, w = 1, 
                           tau = 1, tautil = -1, eps = -1, fctr=1e8)

_A_r_g_u_m_e_n_t_s:

       x: matrix of predictors 

       y: response  

   beta0: initial coefficient values 

    kmax: the maximum number of steps 

   TRACE: If TRUE, cggd prints out its progress 

     TRR: If TRUE builds a Threshold Ridge Regression (TRR) model. If
          FALSE builds a Functional Gradient Descent Kernel model. 

      t0: The initial time in the TRR model. 

alpha1,alpha2,w: Values used in kernel generation. 

tau,tautil,eps: Values used in variable selection. 

    fctr: Used to tune the search routine. Smaller values tighten the
          search (more sampling). 

_D_e_t_a_i_l_s:

     CGGD is a general method for performing different types of
     regressions while continuously varying regularization  and other
     parameters (similar to LARS but broader). It returns the models
     fit along the continuous paths of parameter modification. Note
     that y is automatically centered and x is centered and normalized.

_V_a_l_u_e:

     A "cggd" object is returned, for which plot and predict methods
     exist.

     Some significant componenets of the object are: 

 beta.tk: The coefficients of the model at the different steps (k).

      tk: The _time_ at the different steps (k).

a.set.tk: The active coefficients of the model at the different steps
          (k).

_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>.

_S_e_e _A_l_s_o:

     plot, and predict methods for cggd, and  cv.cggd

_E_x_a_m_p_l_e_s:

     data(housing)
     attach(housing)
     object <- cggd(x,y)
     plot(object)
     object2 <- cggd(x,y,TRR=TRUE)
     plot(object2)
     detach(housing)

