hyperparam.J {ClusTorus} | R Documentation |
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, ...)
icp.torus.objects |
a list whose elements are icp.torus objects, generated by
|
option |
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. |
x |
|
... |
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.
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")