Performs consensus clustering to identify stable clusters.
Usage
consensus_cluster(
d,
maxK = 6,
reps = 1000,
pItem = 0.8,
pFeature = 1,
clusterAlg = "hc",
innerLinkage = "ward.D2",
finalLinkage = "ward.D2",
distance = "euclidean",
seed = NULL,
verbose = FALSE
)Arguments
- d
Data matrix (features x samples) or distance object.
- maxK
Maximum number of clusters to evaluate (default: 6).
- reps
Number of resampling iterations (default: 1000).
- pItem
Proportion of items to sample in each iteration (default: 0.8).
- pFeature
Proportion of features to sample (default: 1).
- clusterAlg
Clustering algorithm: "hc", "km", or "pam" (default: "hc").
- innerLinkage
Linkage method for hierarchical clustering (default: "ward.D2").
- finalLinkage
Linkage for final clustering (default: "ward.D2").
- distance
Distance metric (default: "euclidean").
- seed
Random seed for reproducibility.
- verbose
Print progress messages.