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)

