kde.torus: Kernel density estimation using circular von Mises...

In Hong-Seungki/routine: The Clustering Methods on the Surface of Torus

Description

kde.torus() returns a kde using independent bivariate von mises kernel.

Usage

kde.torus(data, eval.point = grid.torus(), concentration = 25)

Arguments

data	n x 2 matrix of toroidal data on [-π, π)^2
eval.point	N x N numeric matrix on [-π, π)^2. Default input is grid.torus.
concentration	positive number which has the role of κ of von Mises distribution. Default value is 25.

Value

kde.torus returns N-vector of kdes evaluated at eval.point

See Also

grid.torus

Examples

## Not run: 
data <- matrix(c(-pi/3, -pi/3, pi/2, pi/4),
               nrow = 2, byrow = TRUE)

kde.torus(data)

## End(Not run)

Hong-Seungki/routine documentation built on Aug. 23, 2020, 12:42 a.m.