icp.torus.eval: Inductive prediction sets for each level

In sungkyujung/ClusTorus: Prediction and Clustering on the Torus by Conformal Prediction

Inductive prediction sets for each level

Description

icp.torus.eval evaluates whether each pre-specified evaluation point is contained in the inductive conformal prediction sets for each given level.

Usage

icp.torus.eval(icp.torus, level = 0.1, eval.point = grid.torus())

Arguments

icp.torus	an object containing all values to compute the conformity score, which will be constructed with icp.torus.
level	either a scalar or a vector, or even NULL. Default value is 0.1.
eval.point	N x N numeric matrix on [0, 2π)^2. Default input is grid.torus.

Value

returns a cp object with the boolean values which indicate whether each evaluation point is contained in the inductive conformal prediction sets for each given level.

References

Jung, S., Park, K., & Kim, B. (2021). Clustering on the torus by conformal prediction. The Annals of Applied Statistics, 15(4), 1583-1603.

See Also

grid.torus, icp.torus

Examples

data <- toydata1[, 1:2]

icp.torus <- icp.torus(data, model = "kmeans",
                       mixturefitmethod = "general",
                       J = 4, concentration = 25)

icp.torus.eval(icp.torus, level = c(0.1, 0.08), eval.point = grid.torus())

sungkyujung/ClusTorus documentation built on April 30, 2022, 9:18 p.m.