binning                  package:sm                  R Documentation

_C_o_n_s_t_r_u_c_t _f_r_e_q_u_e_n_c_y _t_a_b_l_e _f_r_o_m _r_a_w _d_a_t_a

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

     Given a vector or a matrix `x', this function constructs a
     frequency table associated to appropriate intervals covering the
     range of x.

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

     binning(x, breaks, nbins)

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

       x: a vector or a matrix with either one or two columns.  If `x'
          is a one-dimentional matrix, this is equivalent to a vector. 

  breaks: either a vector or a matrix with two columns, assigning the
          division points of the axis, or the axes in the matrix case.
          If `breaks' is not given, it is computed by dividing the
          range of `x' into `nbins' intervals for each of the axes. 

   nbins: the number of intervals on the `x' axis (in the vector case),
           or a vector of two elements with the number of intervals on
          each axes of `x' (in the marix case). If `nbins' is not
          given, a value is computed as `round(log(length(x),2)+1)' or
          using a similar expression in the matrix case. 

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

     This function is primarity intended for use in connection with
     `sm.density', to estimate noparametrically a density function,
     when the number of data  points is high.  To avoid lengthy
     computations and use of very large matrices,  the data are
     tabulated with the use of `binning', and the outcome is passed to
     `sm.density  ' which computes the estimated density curve, using
     methods described in Chapter 1 of the reference below.

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

     in the vector case, this is a list containing the vector
     `midpoints'  of the interval midpoints and the frequecies `freq'
     associated to them; in the matrix case, the returned value is a
     list with the following  elements: a two-dimensional matrix `x'
     with the coordinates of the midpoints of the two-dimensional bins
     excluding those with 0 frequecies,  its associated matrix `x.freq'
     of frequencies, the coodinateds of the  `midpoints', the division
     points, and the observed frequencies `freq.table' in full tabular
     form.

_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:

     `sm.density', `cut',`table'

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

     # example of 1-d use
     x<-rnorm(1000)
     xb<-binning(x)
     sm.density(xb$x,h=hnorm(x),weights=xb$freq)
     # example of 2-d use
     x<-rnorm(1000)
     x<-cbind(x,x+rnorm(1000))
     xb<-binning(x)
     plot(x)
     sm.density(xb$x, h=hnorm(x), weights=xb$x.freq, display="slice", add=T)

