| 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 = 401, range.x, truncate = TRUE)
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, extended by the
support of the kernel.
|
truncate |
logical flag: if TRUE, data with x values outside the
range specified by range.x are ignored.
|
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 x values at which the estimate was computed.
|
y |
vector of density estimates
at the corresponding x.
|
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.
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")