coclusterContingency {blockcluster} | R Documentation |
This function performs Co-Clustering (simultaneous clustering of rows and columns ) for Contingency data-sets using latent block models.It can also be used to perform semi-supervised co-clustering.
coclusterContingency( 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, numeric vector for row effects and numeric vector column effects in case of contingency data with known row and column effects.) | |||||||||||||||||
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 |
Integer Vector specifying the class of rows. The class number starts from zero. Provide -1 for unknown row class. | |||||||||||||||||
collabels |
Integer 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 Poisson 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 contingency data ## load data data(contingencydataunknown) ## usage of coclusterContingency function in its most simplest form strategy = coclusterStrategy( nbinititerations = 5, nbxem = 2, nbiterations_int = 2 , nbiterationsxem = 10, nbiterationsXEM = 100, epsilonXEM=1e-5) out<-coclusterContingency( contingencydataunknown, nbcocluster=c(2,3), strategy = strategy) ## Summarize the output results summary(out) ## Plot the original and Co-clustered data plot(out)