Description Usage Arguments Details Value
Use spectral clustering as the clustering function of consensus clustering on the provided affinity matrix.
1 2 3 4 5 6 7 | consensus_spectral_clustering(affinity_matrix, cluster_number_max = 5,
reps = 10, pItem = 0.8, pFeature = 1,
title = "Consensus spectral clustering", innerLinkage = "average",
finalLinkage = "average", distance = "pearson", ml = NULL,
tmyPal = NULL, seed = NULL, plot = NULL, writeTable = FALSE,
weightsItem = NULL, weightsFeature = NULL, verbose = F,
corUse = "everything")
|
affinity_matrix |
affinity matrix (e.g. produced by snf or anf) |
cluster_number_max |
integer. The maximum cluster number to evaluate. |
reps |
Number of subsamples evaluated. |
pItem |
numeric, proportion of items to sample. |
pFeature |
numeric, proportion of features to sample. |
title |
character for output directory. Directory is created only if plot is not NULL or writeTable is TRUE. This title can be an abosulte or relative path. |
innerLinkage |
heirarchical linkage method for subsampling. |
finalLinkage |
heirarchical linkage method for consensus matrix. |
ml |
optional. prior result, if supplied then only do graphics and tables. |
tmyPal |
optional character vector of colors for consensus matrix |
seed |
optional numerical value. sets random seed for reproducible results. |
plot |
character value. NULL - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets. |
writeTable |
logical value. TRUE - write ouput and log to csv. |
weightsItem |
optional numerical vector. weights to be used for sampling items. |
weightsFeature |
optional numerical vector. weights to be used for sampling features. |
verbose |
boolean. If TRUE, print messages to the screen to indicate progress. This is useful for large datasets. |
corUse |
optional character value. specifies how to handle missing data in correlation distances 'everything','pairwise.complete.obs', 'complete.obs' see cor() for description. |
It uses ConsensusClusterPlus
.
ConsensusClusterPlus implements the Consensus Clustering algorithm of Monti,
et al (2003). The function will subsamples the affinity matrix according
to pItem, pFeature, weightsItem, and weightsFeature, and clusters the data
into 2 to maxK clusters using spectral clustering.
It will also compute the item consensus results using
calcICL
. For more informations, see the
documentation of the original package!
results of ConsensusClusterPlus & calcICL.
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