CSPA: Cluster-based Similarity Partitioning Algorithm (CSPA)

Description Usage Arguments Value Author(s) See Also Examples

View source: R/consensus_funs.R

Description

Performs hierarchical clustering on a stack of consensus matrices to obtain consensus class labels.

Usage

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CSPA(E, k)

Arguments

E

is an array of clustering results.

k

number of clusters

Value

cluster assignments for the consensus class

Author(s)

Derek Chiu

See Also

Other consensus functions: LCE, k_modes, majority_voting

Examples

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data(hgsc)
dat <- hgsc[1:100, 1:50]
x <- consensus_cluster(dat, nk = 4, reps = 4, algorithms = c("hc", "diana"),
progress = FALSE)
CSPA(x, k = 4)

Example output

  [1] 1 2 3 2 3 1 1 3 3 1 3 3 1 1 2 3 1 1 3 1 1 1 1 3 3 1 1 1 1 3 1 1 1 3 1 1 3
 [38] 3 2 3 1 3 3 3 3 3 3 3 1 1 3 3 1 1 1 3 3 1 3 1 1 1 3 1 1 1 1 3 3 1 3 1 1 3
 [75] 1 3 3 1 2 4 3 1 3 3 1 3 3 1 2 3 3 3 1 3 3 1 1 3 1 3
Warning message:
at_depth() is deprecated, please use `modify_depth()` instead 

diceR documentation built on June 11, 2018, 5:04 p.m.