cluster.assign.torus {ClusTorus}  R Documentation 
Clustering by connected components of ellipsoids
Description
cluster.assign.torus
returns clustering assignment for data
given icp.torus
objects, which can be constructed with
icp.torus
.
plot.clus.torus
plots clustering results, which is given by cluster.obj
object, with some options.
Usage
cluster.assign.torus(icp.object, data = NULL, level = NULL)
## S3 method for class 'cluster.obj'
plot(
x,
assignment = c("outlier", "log.density", "posterior", "mahalanobis"),
overlay = FALSE,
out = FALSE,
...
)
Arguments
icp.object 
an object must be an 
data 
n x d matrix of toroidal data on 
level 
a scalar in 
x 

assignment 
A string. One of "outlier", "log.density", "posterior", "mahalanobis". Default is "outlier". 
overlay 
A boolean index which determines whether plotting ellipseintersections on clustering plots.
Default is 
out 
An option for returning the ggplot object. Default is 
... 
additional parameter for ggplot2::ggplot() 
Value
clustering assignment for data, given icp.torus
objects
cluster.id.by.log.density
cluster assignment result based on approximate logdensity.
cluster.id.by.posterior
cluster assignment result based on the posterior probability.
cluster.id.outlier
cluster assignment result which regards data not included in conformal prediction set as outliers.
cluster.id.by.Mah.dist
cluster assignment result based on Mahalanobis distance.
level
used level which determines the size of clusters(conformal prediction set).
data
input data which are assigned to each cluster.
icp.torus
icp.torus
object which is used for cluster assignment.
References
Jung, S., Park, K., & Kim, B. (2021). Clustering on the torus by conformal prediction. The Annals of Applied Statistics, 15(4), 15831603.
Gilitschenski, I., & Hanebeck, U. D. (2012, July). A robust computational test for overlap of two arbitrarydimensional ellipsoids in faultdetection of kalman filters. In 2012 15th International Conference on Information Fusion (pp. 396401). IEEE.
See Also
Examples
data < toydata1[, 1:2]
icp.torus < icp.torus(data, model = "kmeans",
kmeansfitmethod = "general",
J = 4, concentration = 25)
level < 0.1
cluster.assign.torus(icp.torus, level = level)