hnorm                   package:sm                   R Documentation

_N_o_r_m_a_l _o_p_t_i_m_a_l _c_h_o_i_c_e _o_f _s_m_o_o_t_h_i_n_g _p_a_r_a_m_e_t_e_r _i_n _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:

     This functions evaluates the smoothing parameter which is
     asymptotically optimal for estimating a density function when the
     underlying distribution is Normal.  Data in one, two or three
     dimensions can be handled.

_U_s_a_g_e:

     hnorm(x, weights)

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

       x: a vector, or matrix with two or three columns, containing the
          data. 

 weights: an optional vector of integer values which allows the kernel
          functions over the observations to take different weights
          when they are averaged to produce a density estimate.  This
          is useful, in particular, for censored data and to construct
          an estimate from binned data.  

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

     See Section 2.4.2 of the reference below.

_V_a_l_u_e:

     the value of the asymptotically optimal smoothing parameter for
     Normal case.

_N_o_t_e:

     As from version 2.1 of the package, a similar effect can be
     obtained with the new function 'h.select', via 'h.select(x,
     method="normal", weights=weights)' or simply 'h.select(x)'. Users
     are encouraged to adopt this route, since 'hnorm' might be not
     accessible directly in future releases of the package.

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

     Bowman, A.W. and Azzalini, A. (1997).  _Applied Smoothing
     Techniques for Data Analysis: _ _the Kernel Approach with S-Plus
     Illustrations._ Oxford University Press, Oxford.

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

     'h.select', 'hcv', 'hsj'

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

     x <- rnorm(50)
     hnorm(x)

