dpik {KernSmooth}  R Documentation 
Use direct plugin methodology to select the bandwidth of a kernel density estimate.
dpik(x, scalest = "minim", level = 2L, kernel = "normal", canonical = FALSE, gridsize = 401L, range.x = range(x), truncate = TRUE)
x 
numeric vector containing the sample on which the kernel density estimate is to be constructed. 
scalest 
estimate of scale.

level 
number of levels of functional estimation used in the plugin rule. 
kernel 
character string which determines the smoothing kernel.

canonical 
logical flag: if 
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 
truncate 
logical flag: if 
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")