trainModel,ConsensusMetaclusteringModel-method {PDATK} | R Documentation |
Since consensus clustering is an unsuperived learning method, there isn't
really a 'training step' per se. Instead this method computes the consensus
clusters and stores the results in the models
slot.
## S4 method for signature 'ConsensusMetaclusteringModel' trainModel( object, maxK = 5, reps = 10, distance = "pearson", clusterAlg = "hc", plot = NULL, ... )
object |
A |
maxK |
The maximum number of clusters to test. Defaults to 5. |
reps |
How many random samples should clustering be repeated on? Default is 10, but 1000+ is recommended for real world use. |
distance |
The distance method to use. Defaults to 'pearson'. See
|
clusterAlg |
The clustering algorithm to use. Defaults to 'hc'. See
|
plot |
An optional path to output the plots generated by each
call to |
... |
Fall through parameters to |
The ConsensusMetaclusteringModel
with the clustering results in the
models
slot.