hyperparam.torus {ClusTorus}  R Documentation 
Selecting optimal hyperparameters for the conformal prediction set
Description
hyperparam.torus
selects optimal hyperparameters for constructing the conformal prediction
set, based on the type of postulated model and the criterion.
Usage
hyperparam.torus(
icp.torus.objects,
option = NULL,
alphavec = NULL,
alpha.lim = NULL,
eval.point = NULL
)
## S3 method for class 'hyperparam.torus'
plot(x, color = "auto", ...)
Arguments
icp.torus.objects 
list whose elements are icp.torus objects, generated by

option 
A string. One of "elbow", "risk", "AIC", or "BIC", which determines the
criterion for the model selection. "risk" is based on the negative loglikelihood, "AIC"
for the Akaike Information Criterion, and "BIC" for the Bayesian Information Criterion.
"elbow" is based on minimizing the criterion used in Jung et. al.(2021). Default is

alphavec 
either a scalar or a vector, or even 
alpha.lim 
a positive number lower than 1. Default value is 
eval.point 
N x N numeric matrix on 
x 

color 
A string for plotting 
... 
additional parameter for ggplot2::ggplot() 
Value
returns a list object which contains data.frame
objects for
the evaluated criterion corresponding to each hyperparameter,
selected hyperparameters based on the designated criterion, and
an icp.torus
object based the selected hyperparameters.
References
Jung, S., Park, K., & Kim, B. (2021). Clustering on the torus by conformal prediction. The Annals of Applied Statistics, 15(4), 15831603.
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716723.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461464.
Examples
data < toydata2[, 1:2]
n < nrow(data)
split.id < rep(2, n)
split.id[sample(n, floor(n/2))] < 1
Jvec < 3:35
icp.torus.objects < icp.torus(data, split.id = split.id, model = "kmeans",
kmeansfitmethod = "ge", init = "h",
J = Jvec, verbose = TRUE)
hyperparam.torus(icp.torus.objects, option = "risk")