supsmu                 package:stats                 R Documentation

_F_r_i_e_d_m_a_n'_s _S_u_p_e_r_S_m_o_o_t_h_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     Smooth the (x, y) values by Friedman's 'super smoother'.

_U_s_a_g_e:

     supsmu(x, y, wt, span = "cv", periodic = FALSE, bass = 0)

_A_r_g_u_m_e_n_t_s:

       x: x values for smoothing

       y: y values for smoothing

      wt: case weights, by default all equal

    span: the fraction of the observations in the span of the running
          lines smoother, or '"cv"' to choose this by leave-one-out
          cross-validation.

periodic: if 'TRUE', the x values are assumed to be in '[0, 1]' and of
          period 1.

    bass: controls the smoothness of the fitted curve. Values of up to
          10 indicate increasing smoothness.

_D_e_t_a_i_l_s:

     'supsmu' is a running lines smoother which chooses between three
     spans for the lines. The running lines smoothers are symmetric,
     with 'k/2' data points each side of the predicted point, and
     values of 'k' as '0.5 * n', '0.2 * n' and '0.05 * n', where 'n' is
     the number of data points.  If 'span' is specified, a single
     smoother with span 'span * n' is used.

     The best of the three smoothers is chosen by cross-validation for
     each prediction. The best spans are then smoothed by a running
     lines smoother and the final prediction chosen by linear
     interpolation. 

     The FORTRAN code says: "For small samples ('n < 40') or if there
     are substantial serial correlations between observations close in
     x-value, then a pre-specified fixed span smoother ('span > 0')
     should be used.  Reasonable span values are 0.2 to 0.4."

     Cases with non-finite values of 'x', 'y' or 'wt' are dropped, with
     a warning.

_V_a_l_u_e:

     A list with components 

       x: the input values in increasing order with duplicates removed.

       y: the corresponding y values on the fitted curve.

_R_e_f_e_r_e_n_c_e_s:

     Friedman, J. H. (1984) SMART User's Guide. Laboratory for
     Computational Statistics, Stanford University Technical Report No.
     1.

     Friedman, J. H. (1984) A variable span scatterplot smoother.
     Laboratory for Computational Statistics, Stanford University
     Technical Report No. 5.

_S_e_e _A_l_s_o:

     'ppr'

_E_x_a_m_p_l_e_s:

     require(graphics)

     with(cars, {
         plot(speed, dist)
         lines(supsmu(speed, dist))
         lines(supsmu(speed, dist, bass = 7), lty = 2)
         })