Contour plot of spherical data using a von Mises-Fisher kernel density estimate.
vmf.kerncontour(u, thumb = "none", den.ret = FALSE, full = FALSE, ngrid = 100)
The contour lines of the data. If "den.ret" was set to TRUE a list including:
The latitude values.
The longitude values.
The optimal bandwidth.
The kernel density estimate contour points.
A two column matrix. The first coolumn is the latitude and the second is the longitude.
This is either 'none' (defualt), or 'rot' for the rule of thumb suggested by Garcia-Portugues (2013).
If it is "none" it is estimated via cross validation, with the fast function vmfkde.tune
.
If FALSE (default), plots the contours of the density along with the individual points. If TRUE, will instead return a list with the Longitudes, Latitudes and Densities. Look at the 'value' section for details.
If FALSE (default), uses the range of positions from 'u' to calculate and optionally plot densities. If TRUE, calculates densities covering the entire sphere.
Sets the resolution of the density calculation.
Michail Tsagris, Micah J. Waldstein and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr, Micah J. Waldstein micah@waldste.in and Christos Adam pada4m4@gmail.com.
It calculates the contour plot using a von Mises-Fisher kernel for spherical data only.
Garcia Portugues, E. (2013). Exact risk improvement of bandwidth selectors for kernel density estimation with directional data. Electronic Journal of Statistics, 7, 1655--1685.
vmf.kde, vmfkde.tune, vmf.contour
x <- rvmf(100, rnorm(3), 15)
x <- euclid.inv(x)
# \donttest{
vmf.kerncontour(x, "rot")
# }
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