cp.torus.kde: Conformal prediction set indices with kernel density...

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

Description

cp.torus.kde computes conformal prediction set indices (TRUE if in the set) using kernel density estimation as conformal scores.

Usage

cp.torus.kde(data, eval.point = grid.torus(), level = 0.1, 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.
level	either a scalar or a vector, or even NULL. Default value is 0.1.
concentration	positive number which has the role of κ of von Mises distribution. Default value is 25.

Value

If level is NULL, then return kde at eval.point and at data points.

If level is a vector, return the above and prediction set indices for each value of level.

See Also

kde.torus, grid.torus

Examples

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

cp.torus.kde(data)

## End(Not run)

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