density(x, n=512, kernel="gaussian", bw, adjust=1, width,
from, to, cut=3)
bw.ucv(x, samples=100)
bw.bcv(x, samples=100)
bw.sj(x, samples=100)
print(dobj)
plot(dobj, ...)
x
| the values for which the estimate is to be computed. |
n
| the number of equally spaced points at which the density is to be estimated. This is rounded up to the next power of 2, with a minimum value of 512. |
kernel
|
a character string giving the smoothing kernel to be used.
This must be one of "gaussian", "rectangular", "triangular",
or "cosine", and may be abbrevited to a single letter.
|
bw
|
the smoothing bandwith to be used. This is the standard
deviation of the smoothing kernel. It defaults to 1.06 times the
minimum of the standard deviation and the interquartile range divided by
1.34 times the sample size to the negative one fifth power.
The specified value of bw is multiplied by adjust.
|
adjust
|
the bandwith used is actually adjust*bw.
This makes it easy to specify values like ``half the default'' bandwidth.
|
width
| this exists for compatibility with S. |
from,to
| the left and right-most points of the grid at which the density is to be estimated. |
cut
|
by default, the values of left and right are
cut bandwiths beyond the extremes of the data.
|
samples
|
the sample size to take in the bandwidth
selection functions. If samples is non-positive, the entire
data set is used.
|
dobj
| a ``density'' object. |
...
| plotting parameters. |
density computes kernel density estimates
with the given kernel and bandwidth
(which is the standard deviation of the kernel).
The functions bw.ucv, bw.bcv and bw.sj
provide automated ways of selecting a bandwith for density
estimates (assuming a Gaussian kernel).
The techniques used are as follows; bw.ucv uses unbiased
cross-validation, bw.bcv uses biased cross-validation and
bw.sj uses the technique of Sheather and Jones.
These cross-validation techniques are based on code from the
Venables and Ripley MASS library.
The generic functions plot and print have
methods density objects.
The algorithm used in density disperses the mass of the
empirical distribution function over a regular grid and then
uses the fast Fourier transform to convolve this approximation
with a discretized version of the kernel.
x
y
bw
call
name
x argument.
Venables, W. N. and B. D. Ripley (1994). Modern Applied Statistics with S-Plus. New York: Springer.
Scott, D. W. (1992). Multivariate Density Estimation. Theory, Practice and Visualization. New York: Wiley.
Sheather, S. J. and M. C. Jones (1991). ``A reliable data-based bandwidth selection method for kernel density estimation. J. Roy. Statist. Soc. B, 683-690.
convolve, hist.