hyperparam.J {ClusTorus}R Documentation

Selecting optimal number of mixture components based on various criteria


hyperparam.J evaluates criterion for each icp.torus objects, and select the optimal number of mixture components based on the evaluated criterion.


hyperparam.J(icp.torus.objects, option = c("risk", "AIC", "BIC"))

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



a list whose elements are icp.torus objects, generated by icp.torus.


a string one of "risk", "AIC", or "BIC", which determines the criterion for the model selection. "risk" is based on the negative log-likelihood, "AIC" for the Akaike Information Criterion, and "BIC" for the Bayesian Information Criterion.


hyperparam.J object


additional parameter for ggplot2::ggplot()


returns a hyperparam.J object which contains a data.frame for the evaluated criterion corresponding to each number of components, the optimal number of components, and the corresponding icp.torus object.


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

Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.

Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.

See Also

icp.torus, hyperparam.torus, hyperparam.alpha


data <- toydata1[,1:2]
n <- nrow(data)
split.id <- rep(2,n)
split.id[ sample(n,floor(n/2)) ] <- 1

Jvec = 4:20
icp.torus.objects <- icp.torus(data, split.id = split.id, model = "kmeans", J = Jvec)

hyperparam.J(icp.torus.objects, option = "AIC")

[Package ClusTorus version 0.2.2 Index]