dpik               package:KernSmooth               R Documentation

_S_e_l_e_c_t _a _B_a_n_d_w_i_d_t_h _f_o_r _K_e_r_n_e_l _D_e_n_s_i_t_y _E_s_t_i_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Use direct plug-in methodology to select the bandwidth of a kernel
     density estimate.

_U_s_a_g_e:

     dpik(x, scalest="minim", level=2, kernel="normal",   
          canonical=FALSE, gridsize=401, range.x=range(x), 
          truncate=TRUE)

_A_r_g_u_m_e_n_t_s:

       x: vector containing the sample on which the kernel density
          estimate 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. 

  kernel: character string which determines the smoothing kernel.
          'kernel' can be: '"normal"' - the Gaussian density function
          (the default). '"box"' - a rectangular box. '"epanech"' - the
          centred beta(2,2) density. '"biweight"' - the centred
          beta(3,3) density. '"triweight"' - the centred beta(4,4)
          density. 

canonical: logical flag: if 'TRUE', canonically scaled kernels are used 

gridsize: the number of equally-spaced points over which binning is 
          performed to obtain kernel functional approximation.  

 range.x: vector containing the minimum and maximum values of 'x' at
          which to compute the estimate. The default is the minimum and
          maximum data values. 

truncate: logical flag: if 'TRUE', data with 'x' values outside the
          range specified by 'range.x' are ignored. 

_D_e_t_a_i_l_s:

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

_V_a_l_u_e:

     the selected bandwidth.

_B_a_c_k_g_r_o_u_n_d:

     This method for selecting the bandwidth of a kernel density
     estimate was proposed by Sheather and Jones (1991) and is
     described in Section 3.6 of Wand and Jones (1995).

_R_e_f_e_r_e_n_c_e_s:

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

     Wand, M. P. and Jones, M. C. (1995). _Kernel Smoothing._ Chapman
     and Hall, London.

_S_e_e _A_l_s_o:

     'bkde', 'density', 'ksmooth'

_E_x_a_m_p_l_e_s:

     data(geyser, package="MASS")
     x <- geyser$duration
     h <- dpik(x)
     est <- bkde(x,bandwidth=h)
     plot(est,type="l")

