hyperparam.alpha: Selecting optimal level based on the runs of the number of...

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

Selecting optimal level based on the runs of the number of clusters

Description

hyperparam.alpha evaluates the numbers of clusters for various levels, and select the optimal level based on the runs of the cluster numbers.

Usage

hyperparam.alpha(icp.torus, alphavec = NULL, alpha.lim = 0.15)

## S3 method for class 'hyperparam.alpha'
plot(x, ...)

Arguments

icp.torus	an object containing all values to compute the conformity score, which will be constructed with icp.torus.score.
alphavec	either a scalar or a vector, or even NULL for the levels. Default value is NULL. If NULL, then alphavec is automatically generated as a sequence from 0 to alpha.lim.
alpha.lim	a positive number lower than 1, which is the upper bound of Default is 0.15.
x	hyperparam.alpha object
...	additional parameter for ggplot2::ggplot()

Value

returns a hyperparam.alpha object which contains a data.frame for the numbers of clusters corresponding to the levels and the optimal level.

See Also

hyperparam.J, hyperparam.torus icp.torus

Examples

data <- toydata2[, 1:2]
n <- nrow(data)
split.id <- rep(2, n)
split.id[sample(n, floor(n/2))] <- 1
icp.torus <- icp.torus(data, split.id = split.id, model = "kmeans",
                       kmeansfitmethod = "ge", init = "h",
                       J = 25, verbose = TRUE)
hyperparam.alpha(icp.torus)

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