compute.kernel.estimate: Kernel estimate over grid
In retistruct: Retinal Reconstruction Program

Description Usage Arguments Value Author(s)

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).

1	compute.kernel.estimate(Dss, phi0, fhat, compute.conc)

`Dss`	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.
`phi0`	Rim angle in radians
`fhat`	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.
`compute.conc`	Function to return the optimal value of the concentration parameter kappa given the data.

A list containing

`kappa`	The concentration parameter
`h`	A pseudo-bandwidth parameter, the inverse of the square root of `kappa`. Units of degrees.
`flevels`	Contour levels.
`labels`	Labels of the contours.
`g`	Raw 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`).
`gpa`	Raw density estimate drawn on area-preserving projection. Comprises same elements as above.
`contour.areas`	Area of each individual contour. One level may have more than one contour; this shows the areas of all such contours.
`tot.contour.areas`	Data frame containing the total area within the contours at each level.