predict.plus              package:plus              R Documentation

_E_x_t_r_a_c_t _c_o_e_f_f_i_c_i_e_n_t_s _a_n_d _m_a_k_e _p_r_e_d_i_c_t_i_o_n_s _f_r_o_m _a _p_l_u_s _s_o_l_u_t_i_o_n _p_a_t_h.

_D_e_s_c_r_i_p_t_i_o_n:

     While plus() produces turning points in a path of critical points
     of a penalized squared loss,  predict.plus extracts coefficients
     and make predictions at particular penalty levels by linear 
     interpolation. The extracted coefficients are the same as these
     produced by plus() when the  input lam is the same.

_U_s_a_g_e:

     ## S3 method for class 'plus':
     predict(object, lam, newx, ...) 
     ## S3 method for class 'plus':
     coef(object, lam, ...) 

_A_r_g_u_m_e_n_t_s:

  object: A fitted plus object

     lam: A decreasing sequence of nonnegative numbers as penalty
          levels at which the  coefficients are extracted and
          predictions are required. Default is the ordered values  of
          lam.path generated by plus().  

    newx: x values at which the fit is required. If newx is not set,
          coefficients are extacted  but predictions are not produced.

     ...: Additonal arguments for generic methods

_V_a_l_u_e:

     A list containing the following items:  

  lambda: penalty levels at which the coefficients and predicted values
          are extracted.

coefficients: extracted coefficients.

dimension: number of nonzero coefficients.

r.square: R-square as the ratio of the total centered residual sum of
          squares and the  total centered sum of squares. 

    step: number of plus steps required to compute the coefficients.

  method: including the LASSO, MC+, and SCAD.

    newy: extracted predictions at newx; not produced if newx is not
          supplied.

     ...: Additonal arguments for generic methods

_A_u_t_h_o_r(_s):

     Cun-Hui Zhang and Ofer Melnik

_R_e_f_e_r_e_n_c_e_s:

     Zhang, C.-H. (2007). Penalized linear unbiased selection.
     Technical Report No. 2007-003.  Department of Statistics, Rutgers
     University.

_S_e_e _A_l_s_o:

     print, plot, plus

_E_x_a_m_p_l_e_s:

     data(sp500)
     attach(sp500)

     x <- sp500.percent[,3: (dim(sp500.percent)[2])] 
     y <- sp500.percent[,1]

     object <- plus(x,y,method="mc+")
     ## extract coefficients for the first 10 values of lam.path at values in x  
     extracted.values <- predict(object, lam = sort(object$lam.path[1:10],decreasing=TRUE), newx=object$x)
     extracted.coef <- coef(object, lam = sort(object$lam.path[1:10],decreasing=TRUE))
     detach(sp500)

