kde {asymmetry.measures} | R Documentation |
Kernel density estimator.
Description
Classical univariate kernel density estimator.
Usage
kde(xin, xout, h, kfun)
Arguments
xin |
A vector of data points. Missing values not allowed. |
xout |
A vector of grid points at which the estimate will be calculated. |
h |
A scalar, the bandwidth to use in the estimate, e.g. |
kfun |
Kernel function to use. |
Details
Implements the classical density kernel estimator based on a sample X_1,X_2,.., X_n
of i.i.d observations from a distribution F
with density h
. The estimator is defined by
\hat{f}(x)= n^{-1}\sum_{i=1}^n K_h(x-X_{i})
where h
is determined by a bandwidth selector such as Silverman's default plug-in rule and K
, the kernel, is a non-negative probability density function.
Value
A vector with the density estimates at the designated points xout.
Author(s)
Dimitrios Bagkavos and Lucia Gamez Gallardo
R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com> , Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>
References
See Also
bw.nrd
, bw.nrd0
, bw.ucv
, bw.bcv
Examples
x.in <- rnorm(100)
x.out <- seq(-3.4,3.4,length=60)
bandwidth <- bw.nrd(x.in)
kernel <- Epanechnikov
dens.est <- kde(x.in,x.out,bandwidth,kernel)
plot(x.out,dens.est,col="red",main="Kernel density estimator")