plot.plus                package:plus                R Documentation

_P_l_o_t _m_e_t_h_o_d _f_o_r _p_l_u_s _o_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Produce a plot from a plus solution path.

_U_s_a_g_e:

     ## S3 method for class 'plus':
     plot(x, xvar=c("lam","step"), yvar=c("coef","newy","lam","dim","R-sq"), 
             newx, step.interval, lam.interval, predictors, ...)

_A_r_g_u_m_e_n_t_s:

       x: a plus object

    xvar: penalty level or plus step as the variable for the horizontal
          axis in the plot. Default is "lam".

    yvar: paths of coefficients, predictions, penalty level, the number
          of nonzero coefficients or R-square  as the variable for the
          vertical axis in the plot. Default is "coef"

    newx: x values at which the fit is required. If newx is not set and
          yvar is "newy", no plot is produced.

step.interval: lower and upper bounds of the x-axis in the plot when
          xvar is "step". Defult covers all  steps in the computed
          path. 

lam.interval: lower and upper bounds of the x-axis in the plot when
          xvar is "lam".  Default covers  all penalty levels in the
          computed path.

predictors: a subset of predictors for which coefficients are plotted.
          Default is the entire set of predictors.

     ...: Additonal arguments for generic methods

_D_e_t_a_i_l_s:

     The fitted coefficients and penalty levels are linear between two
     consecutive turning points in the plus path  so that exact values
     of "coef", "newy" and "lam" are ploted when xvar is set as "step".
     For concave penalties,  the solution path is not necessarily
     monotone in penalty level. Since the extracted coefficients for a
     particular  given penalty level is defined as the first point at
     which the solution path hits the given penalty level, the  "coef"
     and "newy" plotted as approximations as the linear interpolation
     of their actual values at specifiec  lam when xvar is set as
     "lam".

_V_a_l_u_e:

     NULL

_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.

_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+")
     plot(object)
     detach(sp500)

