View source: R/CC_with_robScore_functions.R
multiview_consensus_matrix | R Documentation |
Calculate consensus matrix for multi-data consensus clustering
multiview_consensus_matrix(
X,
max.cluster = 5,
sample.set = NA,
clustering.method = "hclust",
adj.conv = TRUE,
verbos = TRUE
)
X |
list of adjacency matrices for different cohorts (or views). |
max.cluster |
maximum number of clusters |
sample.set |
vector of samples the clustering is being applied on. |
clustering.method |
base clustering method: |
adj.conv |
binary value to apply soft threshold (default= |
verbos |
binary value for verbosity (default= |
performs multi-data consensus clustering and obtain consensus matrix Monti et al. (2003) consensus clustering algorithm
description list of consensus matrices for each k
data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Adj = list()
for (i in 1:length(X_observation))
Adj[[i]] = adj_mat(X_observation[[i]], method = "euclidian")
CM = multiview_consensus_matrix(Adj, max.cluster = 4, verbos = FALSE)
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