This function is used to perform circular kernel density estimation on the
sample data set in order to obtain the minimum points of the kernel density estimator.
Usage
circ.kernel(data, sp, to = 1, grid = 512, m = 1)
Arguments
data
the data vector from which the circular kernel density estimator is to be computed.
sp
a real value $(0 < sp < 1)$ for the smoothing parameter to be used. This value can be
obtained by using findh.
to
the value of the maximum domain of the data. Values will usually
either be 1 or 2$\pi$.
grid
the number of equally spaced grid points at which the density is to be estimated.
m
the number of local minimum points included in the output.
Value
a list containing the following components:
xa vector of sorted $x$ values that represents the equally-spaced grid points used during the kernel
density estimation.
ya vector of density-values of the circular kernel density estimator corresponding to $x$.
minimuma vector of the kernel grid point(s) of lowest density
derived from the circular kernel density estimator. The length of the vector will depend on the choice of m.
Details
The Epanechnikov kernel function is used in the circular kernel density estimation.
Circular kernel density estimation is perform according to the method proposed in 'Topics in circular statistics' (see references).
References
Jammalamadaka S, SenGupta A (2001). Topics in circular statistics. World Scientific Publishing
Co. Pte. Ltd.