coclusterContinuous {blockcluster}

Co-Clustering function.

Description

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

Usage

coclusterContinuous(
  data,
  semisupervised = FALSE,
  rowlabels = integer(0),
  collabels = integer(0),
  model = NULL,
  nbcocluster,
  strategy = coclusterStrategy(),
  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

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 Gaussian data:

Model	Data-type	Proportions	Dispersion/Variance
pik_rhol_sigma2kl(Default)	continuous	unequal	unequal
pik_rhol_sigma2	continuous	unequal	equal
pi_rho_sigma2kl	continuous	equal	unequal
pi_rho_sigma2	continuous	equal	equal

nbcocluster

Integer vector specifying the number of row and column clusters respectively.

strategy

Object of class strategy.

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 continuous data
#load data
data(gaussiandata)
#usage of coclusterContinuous function in its most simplest form
out<-coclusterContinuous(gaussiandata,nbcocluster=c(2,3))
#Summarize the output results
summary(out)
#Plot the original and Co-clustered data 
plot(out)