Returns an estimate of a binned approximation to the kernel estimate of the specified density functional. The kernel is the standard normal density.
bkfe(x, drv, bandwidth, gridsize = 401L, range.x, binned = FALSE,
truncate = TRUE)the (scalar) estimated functional.
numeric vector of observations from the distribution whose density is to be estimated. Missing values are not allowed.
order of derivative in the density functional. Must be a non-negative even integer.
the kernel bandwidth smoothing parameter. Must be supplied.
the number of equally-spaced points over which binning is performed.
vector containing the minimum and maximum values of x
at which to compute the estimate.
The default is the minimum and maximum data values, extended by the
support of the kernel.
logical flag: if TRUE, then x and y are taken to be grid counts
rather than raw data.
logical flag: if TRUE, data with x values outside the
range specified by range.x are ignored.
Estimates of this type were proposed by Sheather and Jones (1991).
The density functional of order drv is the integral of the
product of the density and its drvth derivative.
The kernel estimates
of such quantities are computed using a binned implementation,
and the kernel is the standard normal density.
Sheather, S. J. and Jones, M. C. (1991). A reliable data-based 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
est <- bkfe(x, drv=4, bandwidth=0.3)
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