bkde               package:KernSmooth               R Documentation

_C_o_m_p_u_t_e _a _B_i_n_n_e_d _K_e_r_n_e_l _D_e_n_s_i_t_y _E_s_t_i_m_a_t_e

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

     Returns x and y coordinates of the binned kernel density estimate
     of the probability density of the data.

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

     bkde(x, kernel="normal", canonical=FALSE, bandwidth,
          gridsize=401, range.x=range(x), truncate=TRUE)

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

       x: vector of observations from the distribution whose density is
          to be estimated. Missing values are not allowed. 

bandwidth: the kernel bandwidth smoothing parameter. Larger values of
          `bandwidth' make smoother estimates, smaller values of
          `bandwidth' make less smooth estimates. 

  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 at which to estimate the
          density. 

 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:

     This is the binned approximation to the ordinary kernel density
     estimate. Linear binning is used to obtain the bin counts.   For
     each `x' value in the sample, the kernel is centered on that `x'
     and the heights of the kernel at each datapoint are summed. This
     sum, after a normalization, is the corresponding `y' value in the
     output.

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

     a list containing the following components:

       x: vector of sorted `x' values at which the estimate was
          computed. 

       y: vector of density estimates at the corresponding `x'. 

_B_a_c_k_g_r_o_u_n_d:

     Density estimation is a smoothing operation. Inevitably there is a
     trade-off between bias in the estimate and the estimate's
     variability: large bandwidths will produce smooth estimates that
     may hide local features of the density; small bandwidths may
     introduce spurious bumps into the estimate.

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

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

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

     `density', `dpik', `hist', `ksmooth'.

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

     data(geyser)
     x <- geyser$duration
     est <- bkde(x, bandwidth=0.25)
     plot(est, type="l")

