

binning(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 con-
     structs 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 connec-
     tion 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 meth-
     ods described in Chapter 1 of the reference below.

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

     in the vector case, this is a list containing the vec-
     tor `midpoints' of the interval midpoints and the fre-
     quecies `freq' associated to them; in the matrix case,
     the returned value is a list with the following ele-
     ments: a two-dimensional matrix `x' with the coordi-
     nates 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 fre-
     quencies `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)

