Contour plot of spherical data using a von Mises-Fisher kernel density estimate
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)
u |
A two column matrix. The first coolumn is the latitude and the second is the longitude. |
thumb |
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_2". |
den.ret |
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. |
full |
If FALSE (default), uses the range of positions from 'u' to calculate and optionally plot densities. If TRUE, calculates densities covering the entire sphere. |
ngrid |
Sets the resolution of the density calculation. |
It calcculates the contour plot using a von Mises-Fisher kernel for spherical data only.
The contour lines of the data. If "den.ret" was set to TRUE a list including:
lat |
The latitude values. |
long |
The longitude values. |
h |
The optimal bandwidth. |
den |
The kernel density estimate contour points. |
Michail Tsagris and Micah J. Waldstein.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr, Giorgos Athineou <gioathineou@gmail.com> and Micah J. Waldstein <micah@waldste.in>.
Garcia Portugues, E. (2013). Exact risk improvement of bandwidth selectors for kernel density estimation with directional data. Electronic Journal of Statistics, 7, 1655–1685.
x <- rvmf(100, rnorm(3), 15) x <- euclid.inv(x) par( mfrow = c(1, 2) ) vmf.kerncontour(x, "rot") vmf.kerncontour(x, "none")
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