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blockcluster (version 4.5.5)

coclusterContinuous: 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
)

Value

Return an object of BinaryOptions or ContingencyOptions

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

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:

ModelData-typeProportionsDispersion/Variance
pik_rhol_sigma2kl(Default)continuousunequalunequal
pik_rhol_sigma2continuousunequalequal
pi_rho_sigma2klcontinuousequalunequal
pi_rho_sigma2continuousequalequal

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.

Examples

Run this code

# 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)


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