blockcluster (version 4.0.2)

coclusterBinary: 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 = numeric(0), collabels = numeric(0), model = character(0), nbcocluster, strategy = coclusterStrategy(), a = 1, b = 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

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 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	Model

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).

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)