cp.torus.kde {ClusTorus}R Documentation

Conformal prediction set indices with kernel density estimation

Description

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

Usage

cp.torus.kde(data, eval.point = grid.torus(), level = 0.1, concentration = 25)

Arguments

data

n x d matrix of toroidal data on [0, 2π)^d

eval.point

N x N numeric matrix on [0, 2π)^d. Default input is NULL, which represents the fine grid points on [0, 2π)^d.

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.

References

S. Jung, K. Park, and B. Kim (2021), "Clustering on the torus by conformal prediction"

See Also

kde.torus, grid.torus

Examples

data <- ILE[1:200, 1:2]
cp.torus.kde(data, eval.point = grid.torus(),
             level = 0.05, concentration = 25)

[Package ClusTorus version 0.1.3 Index]