

dpih(KernSmooth)                             R Documentation

_S_e_l_e_c_t _a _H_i_s_t_o_g_r_a_m _B_i_n _W_i_d_t_h

_D_e_s_c_r_i_p_t_i_o_n_:

     Uses direct plug-in methodology to select the bin width
     of a histogram.

_U_s_a_g_e_:

     dpih(x, scalest="minim", level=2, gridsize=401,
          range.x=range(x), truncate=F)

_A_r_g_u_m_e_n_t_s_:

       x: vector containing the sample on which the his-
          togram is to be constructed.

 scalest: estimate of scale.

          `"stdev"' - standard deviation is used.

          `"iqr"' - inter-quartile range divided by 1.349 is
          used.

          `"minim"' - minimum of `"stdev"' and `"iqr"' is
          used.

   level: number of levels of functional estimation used in
          the plug-in rule.

gridsize: number of grid points used in the binned approxi-
          mations to functional estimates.

 range.x: range over which functional estimates are
          obtained.  The default is the minimum and maximum
          data values.

truncate: if `truncate' is `TRUE' then observations outside
          of the interval specified by `range.x' are omit-
          ted.  Otherwise, they are used to weight the
          extreme grid points.

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

     the selected bin width.

_D_e_t_a_i_l_s_:

     The direct plug-in approach, where unknown functionals
     that appear in expressions for the asymptotically opti-
     mal bin width and bandwidths are replaced by kernel
     estimates, is used.  The normal distribution is used to
     provide an initial estimate.

_B_A_C_K_G_R_O_U_N_D_:

     This method for selecting the bin width of a histogram
     is described in Wand (1995). It is an extension of the
     normal scale rule of Scott (1979) and uses plug-in
     ideas from bandwidth selection for kernel density esti-
     mation (e.g. Sheather and Jones, 1991).

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

     Scott, D. W. (1979).  On optimal and data-based his-
     tograms.  Biometrika, 66, 605-610.

     Sheather, S. J. and Jones, M. C. (1991).  A reliable
     data-based bandwidth selection method for kernel den-
     sity estimation.  Journal of the Royal Statistical
     Society, Series B, 53, 683-690.

     Wand, M. P. (1995).  Data-based choice of histogram
     binwidth.  University of New South Wales, Australian
     Graduate School of Management Working Paper Series No.
     95-011.

_S_e_e _A_l_s_o_:

     `hist'

_E_x_a_m_p_l_e_s_:

     data(geyser)
     x <- geyser$duration
     h <- dpih(x)
     bins <- seq(min(x)-0.1,max(x)+0.1+h,by=h)
     hist(x,breaks=bins)

