View source: R/coclusterBinary.R
coclusterBinary | R Documentation |
This function performs Co-Clustering (simultaneous clustering of rows and columns ) for Binary data-sets using latent block models. It can also be used to perform semi-supervised co-clustering.
coclusterBinary(
data,
semisupervised = FALSE,
rowlabels = integer(0),
collabels = integer(0),
model = NULL,
nbcocluster,
strategy = coclusterStrategy(),
a = 1,
b = 1,
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 |
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 Binary data:
| |||||||||||||||||||||
nbcocluster |
Integer vector specifying the number of row and column clusters respectively. | |||||||||||||||||||||
strategy |
Object of class | |||||||||||||||||||||
a |
First hyper-parameter in case of Bayesian settings. Default is 1 (no prior). | |||||||||||||||||||||
b |
Second hyper-parameter in case of Bayesian settings. Default is 1 (no prior). | |||||||||||||||||||||
nbCore |
number of thread to use (OpenMP must be available), 0 for all cores. Default value is 1. |
Return an object of BinaryOptions
.
## Simple example with simulated binary data
## load data
data(binarydata)
## usage of coclusterBinary function in its most simplest form
out<-coclusterBinary(binarydata,nbcocluster=c(2,3))
## Summarize the output results
summary(out)
## Plot the original and Co-clustered data
plot(out)
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