hnorm {sm} | R Documentation |
This functions evaluates the smoothing parameter which is asymptotically optimal for estimating a density function when the underlying distribution is Normal. Data in one, two or three dimensions can be handled.
hnorm(x, weights)
x |
a vector, or matrix with two or three columns, containing the data. |
weights |
a vector which allows the kernel functions over the observations to take different weights when they are averaged to produce a density estimate. This is useful, in particular, for censored data and to construct an estimate from binned data. |
See Section 2.4.2 of the reference below.
the value of the Normal optimal smoothing parameter.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
x <- rnorm(50) hnorm(x)