Description Usage Arguments Details Value Author(s) References See Also Examples
Classical univariate kernel density estimator.
1 | kde(xin, xout, h, kfun)
|
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. |
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}∑_{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.
A vector with the density estimates at the designated points xout.
Dimitrios Bagkavos and Lucia Gamez Gallardo
R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com> , Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>
bw.nrd
, bw.nrd0
, bw.ucv
, bw.bcv
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