density(x, bw, adjust = 1, kernel="gaussian", window = kernel, n = 512, width, from, to, cut = 3, na.rm = FALSE) 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,window
|
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.
|
na.rm
|
logical; if TRUE , missing values are eliminated from
x in advance to further computation.
|
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 generic functions plot
and print
have
methods for 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
| the coordinates of the points where the density is estimated. |
y
| the estimated density values. |
bw
| the bandwidth used. |
n
|
the sample size length(x) .
|
call
| the call which produced the result. |
data.name
|
the deparsed name of the 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
.# The Old Faithful geyser data data(faithful) d <- density(faithful$eruptions, bw=0.15) d plot(d) plot(d, type="n") polygon(d, col="wheat") ## Missing values: x <- xx <- faithful$eruptions x[i.out <- sample(length(x), 10)] <- NA doR <- density(x, bw=0.15, na.rm = TRUE) doN <- density(x, bw=0.15, na.rm = FALSE) lines(doR, col="blue") lines(doN, col="red") points(xx[i.out], rep(.01,10))