coclusterBinary: Co-Clustering function for Binary data.

In blockcluster: Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets

Co-Clustering function for Binary data.

Description

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.

Usage

coclusterBinary(
  data,
  semisupervised = FALSE,
  rowlabels = integer(0),
  collabels = integer(0),
  model = NULL,
  nbcocluster,
  strategy = coclusterStrategy(),
  a = 1,
  b = 1,
  nbCore = 1
)

Arguments

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: Model Data-type Proportions Dispersion/Variance pik_rhol_epsilonkl(Default) binary unequal unequal pik_rhol_epsilon binary unequal equal pi_rho_epsilonkl binary equal unequal pi_rho_epsilon binary equal equal
nbcocluster	Integer vector specifying the number of row and column clusters respectively.
strategy	Object of class strategy.
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.

Value

Return an object of BinaryOptions.

Examples

## 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)

blockcluster documentation built on March 7, 2023, 6:39 p.m.