vmf.kde: Kernel density estimation for (hyper-)spherical data using a...

In Directional: A Collection of Functions for Directional Data Analysis

Kernel density estimation for (hyper-)spherical data using a von Mises-Fisher kernel

Description

A von Mises-Fisher kernel is used for the non parametric density estimation.

Usage

vmf.kde(x, h, thumb = "none")

Arguments

x	A matrix with unit vectors, i.e. the data being expressed in Euclidean cordinates.
h	The bandwidth to be used.
thumb	If this is "none", the given bandwidth is used. If it is "rot" the rule of thumb suggested by Garcia-Portugues (2013) is used.

Details

A von Mises-Fisher kernel is used for the non parametric density estimation.

Value

A list including:

h	The bandwidth used.
f	A vector with the kernel density estimate calculated for each of the data points.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail] and Giorgos Athineou <[e-mail]>.

References

Garcia Portugues, E. (2013). Exact risk improvement of bandwidth selectors for kernel density estimation with directional data. Electronic Journal of Statistics, 7, 1655–1685.

See Also

vmfkde.tune, vm.kde, vmf.mle, vmkde.tune

Examples

x <- rvmf(100, rnorm(5), 15)
h <- vmfkde.tune(x)[1]
f1 <- vmf.kde(x, h = h, thumb = "none")
f2 <- vmf.kde(x, h = h, thumb = "rot")
f1$h  ;  f2$h

Directional documentation built on Oct. 30, 2024, 9:15 a.m.