bkde {KernSmooth}  R Documentation 
Returns x and y coordinates of the binned kernel density estimate of the probability density of the data.
bkde(x, kernel = "normal", canonical = FALSE, bandwidth, gridsize = 401L, range.x, truncate = TRUE)
x 
numeric 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

kernel 
character string which determines the smoothing kernel.

canonical 
logical flag: if 
gridsize 
the number of equally spaced points at which to estimate the density. 
range.x 
vector containing the minimum and maximum values of 
truncate 
logical flag: if 
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.
a list containing the following components:
x 
vector of sorted 
y 
vector of density estimates
at the corresponding 
Density estimation is a smoothing operation. Inevitably there is a tradeoff 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.
Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
data(geyser, package="MASS") x < geyser$duration est < bkde(x, bandwidth=0.25) plot(est, type="l")