| von Mises-Fisher kernel density estimation for (hyper-)spherical data {Directional} | R Documentation |
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 mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>.
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