Compute a kernel estimate over a grid and do a contour analysis
of this estimate. The contour heights the determined by finding
heights that exclude a certain fraction of the probability. For
example, the 95
and it should enclose about 5
are specified by the contour.levels option; by default
they are c(5, 25, 50, 75, 95).
compute.kernel.estimate(Dss, phi0, fhat, compute.conc)A list containing
kappaThe concentration parameter
hA pseudo-bandwidth parameter, the inverse of the square root of kappa. Units of degrees.
flevelsContour levels.
labelsLabels of the contours.
gRaw density estimate drawn on non-area-preserving projection. Comprises locations of gridlines in Cartesian coordinates (xs and ys), density estimates at these points, f and location of maximum in Cartesian coordinates (max).
gpaRaw density estimate drawn on area-preserving projection. Comprises same elements as above.
contour.areasArea of each individual contour. One level may have more than one contour; this shows the areas of all such contours.
tot.contour.areasData frame containing the total area within the contours at each level.
List of datasets. The first two columns of each datasets
are coordinates of points on the sphere in spherical polar
(latitude, phi, and longitude, lambda)
coordinates. In the case kernel smoothing, there is a third column
of values of dependent variables at those points.
Rim angle in radians
Function such as kde.fhat or
kr.yhat to compute the density given data and a
value of the concentration parameter kappa of the Fisher
density.
Function to return the optimal value of the concentration parameter kappa given the data.
David Sterratt