cc_get_cluster: Provides the vector of clusters' ID to which each element...
In CorradoLanera/CrossClustering: A Partial Clustering Algorithm

Description Usage Arguments Value Methods (by class) Author(s) References Examples

Provides the vector of clusters' ID to which each element belong to.

cc_get_cluster(x, n_elem)

## Default S3 method:
cc_get_cluster(x, n_elem)

## S3 method for class 'crossclustering'
cc_get_cluster(x, n_elem)

`x`	list of clustered elements or a `crossclustering` object
`n_elem`	total number of elements clustered (ignored if x is of class `crossclustering`)

An integer vector of clusters to which the elements belong ('1' for the outliers, ID + 1 for the others).

default: default method for cc_get_cluster.
crossclustering: automatically extract inputs from a crossclustering object

Paola Tellaroli, <paola [dot] tellaroli [at] unipd [dot] it>;; Marco Bazzi, <bazzi [at] stat [dot] unipd [dot] it>; Michele Donato, <mdonato [at] stanford [dot] edu>.

Tellaroli P, Bazzi M., Donato M., Brazzale A. R., Draghici S. (2016). Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. PLoS ONE 11(3): e0152333. doi:10.1371/journal.pone.0152333

library(CrossClustering)

data(toy)

### toy is transposed as we want to cluster samples (columns of the
### original matrix)
toy_dist <- t(toy) %>% dist(method = "euclidean")

### Run CrossClustering
toyres <- cc_crossclustering(toy_dist,
  k_w_min = 2,
  k_w_max = 5,
  k2_max  = 6,
  out     = TRUE
)

### cc_get_cluster
cc_get_cluster(toyres[], 7)


### cc_get_cluster directly from a crossclustering object
cc_get_cluster(toyres)