dpik(x, scalest="minim", level=2, kernel="normal", canonical=F, gridsize=401, range.x=range(x), truncate=F)
x
| vector containing the sample on which the kernel density estimate is to be constructed. |
scalest
|
estimate of scale.
|
level
| number of levels of functional estimation used in the plug-in rule. |
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 over which binning is performed to obtain kernel functional approximation. |
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.
|
truncate
|
logical flag: if TRUE , data with x values outside the
range specified by range.x are ignored.
|
Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
bkde
, density
, ksmooth
data(geyser) x <- geyser$duration h <- dpik(x) est <- bkde(x,bandwidth=h) plot(est,type="l")