

supsmu {modreg}                              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 = rep(1, length(y)), 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

    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. Val-
          ues of up to 10 indicating 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 0.5n,
     0.2n and 0.05n. If `span' is specified, as single
     smoother with span `span' * n is used.

     The best of the three smoothers is chosen by cross-val-
     idation for each prediction. The best spans are then
     smoothed by a running lines smoother and the final pre-
     diction 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 prespecified
     fixed span smoother (`span > 0') should be used. Rea-
     sonable span values are 0.2 to 0.4.''

_V_a_l_u_e_:

     A list with components

       x: the input values in increasing order with dupli-
          cates removed.

       y: the corresponding y values on the fitted curve.

_A_u_t_h_o_r_(_s_)_:

     B. D. Ripley

_R_e_f_e_r_e_n_c_e_s_:

     Friedman, J. H. (1984) SMART User's Guide.  Laboratory
     for Computational Statistics, Stanford University Tech-
     nical 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_:

     data(cars)
     attach(cars)
     plot(speed, dist)
     lines(supsmu(speed, dist))
     lines(supsmu(speed, dist, bass=7), lty=2)
     detach()

