dpik {KernSmooth}  R Documentation 
Use direct plugin methodology to select the bandwidth of a kernel density estimate.
dpik(x, scalest="minim", level=2, kernel="normal", canonical=FALSE, gridsize=401, range.x=range(x), truncate=TRUE)
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"  interquartile 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 plugin 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 equallyspaced 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.

The direct plugin 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.
the selected bandwidth.
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).
Sheather, S. J. and Jones, M. C. (1991). A reliable databased 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.
data(geyser, package="MASS") x < geyser$duration h < dpik(x) est < bkde(x,bandwidth=h) plot(est,type="l")