coclusterCategorical {blockcluster}

Co-Clustering function for categorical data-sets.

Description

This function performs Co-Clustering (simultaneous clustering of rows and columns ) Categorical data-sets using latent block models. It can also be used to perform semi-supervised co-clustering.

Usage

coclusterCategorical(
  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 categorical data:

pik_rhol_multi	categorical	unequal	unequal
pi_rho_multi	categorical	equal	unequal

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 or ContingencyOptions or ContinuousOptions depending on whether the data-type is Binary, Contingency or Continuous respectively.

Examples

## Simple example with simulated categorical data
## load data
data(categoricaldata)
## usage of coclusterCategorical function in its most simplest form
out<-coclusterCategorical(categoricaldata,nbcocluster=c(3,2))
## Summarize the output results
summary(out)
## Plot the original and Co-clustered data 
plot(out)