coclusterContinuous {blockcluster} | R Documentation |
This function performs Co-Clustering (simultaneous clustering of rows and columns ) for continuous data-sets using latent block models. It can also be used to perform semi-supervised co-clustering.
coclusterContinuous( data, semisupervised = FALSE, rowlabels = integer(0), collabels = integer(0), model = NULL, nbcocluster, strategy = coclusterStrategy(), nbCore = 1 )
data |
Input data as matrix (or list containing data matrix.) | |||||||||||||||||||||
semisupervised |
Boolean value specifying whether to perform semi-supervised co-clustering or not. Make sure to provide row and/or column labels if specified value is true. The default value is false. | |||||||||||||||||||||
rowlabels |
Vector specifying the class of rows. The class number starts from zero. Provide -1 for unknown row class. | |||||||||||||||||||||
collabels |
Vector specifying the class of columns. The class number starts from zero. Provide -1 for unknown column class. | |||||||||||||||||||||
model |
This is the name of model. The following models exists for Gaussian data:
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nbcocluster |
Integer vector specifying the number of row and column clusters respectively. | |||||||||||||||||||||
strategy |
Object of class | |||||||||||||||||||||
nbCore |
number of thread to use (OpenMP must be available), 0 for all cores. Default value is 1. |
Return an object of BinaryOptions
or ContingencyOptions
or ContinuousOptions
depending on whether the data-type is Binary, Contingency or Continuous
respectively.
# Simple example with simulated continuous data #load data data(gaussiandata) #usage of coclusterContinuous function in its most simplest form out<-coclusterContinuous(gaussiandata,nbcocluster=c(2,3)) #Summarize the output results summary(out) #Plot the original and Co-clustered data plot(out)