Package 'blockcluster' reference manual

Title:	Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets
Description:	Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The 'blockcluster' package provides a bridge between the C++ core library build on top of the 'STK++' library, and the R statistical computing environment. This package allows to co-cluster binary <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>, continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL.
Authors:	Serge Iovleff [aut, cre], Parmeet Singh Bhatia [aut], Josselin Demont [ctb], Vincent Brault [ctb], Vincent Kubicki [ctb], Gerard Goavert [ctb], Christophe Biernacki [ctb], Gilles Celeux [ctb]
Maintainer:	Serge Iovleff <[email protected]>
License:	GPL (>= 3)
Version:	4.5.5
Built:	2024-11-20 07:00:06 UTC
Source:	CRAN

Getter method for blockcluster output

Description

This is overloading of square braces to extract values of various slots of the output from coclusterBinary, coclusterCategorical, coclusterContingency, coclusterContinuous.

Usage

## S4 method for signature 'strategy'
x[i, j, drop]

## S4 method for signature 'BinaryOptions'
x[i, j, drop]

## S4 method for signature 'ContingencyOptions'
x[i, j, drop]

## S4 method for signature 'ContinuousOptions'
x[i, j, drop]

## S4 method for signature 'CategoricalOptions'
x[i, j, drop]
## S4 method for signature 'strategy'
x[i, j, drop]

## S4 method for signature 'BinaryOptions'
x[i, j, drop]

## S4 method for signature 'ContingencyOptions'
x[i, j, drop]

## S4 method for signature 'ContinuousOptions'
x[i, j, drop]

## S4 method for signature 'CategoricalOptions'
x[i, j, drop]

Arguments

`x`	object from which to extract element(s) or in which to replace element(s).
`i`	the name of the element we want to extract or replace.
`j`	if the element designing by i is complex, j specifying elements to extract or replace.
`drop`	not used

Simulated Binary Data-set

Description

It is a binary data-set simulated using Bernoulli distribution. It consist of two clusters in rows and three clusters in columns.

Format

A data matrix with 1000 rows and 100 columns.

Examples

  data(binarydata)
data(binarydata)

This class contains all the input options as well as the estimated paramters for Binary data-set. It inherits from base class CommonOptions. The class contains following output parameters given in 'Details' along with the parameters in base class.

Details

classmean:: The mean value of each co-cluster.
classdispersion:: The dispersion of each co-cluster.
ICLvalue:: Integrated complete likelihood

Co-Clustering Package

Description

This package performs Co-clustering of binary, contingency, continuous and categorical data-sets.

Details

This package performs Co-clustering of binary, contingency, continuous and categorical data-sets with utility functions to visualize the Co-clustered data. The package contains a set of functions coclusterBinary, coclusterCategorical, coclusterContingency, coclusterContinuous which perform Co-clustering on various kinds of data-sets and return object of appropriate class (refer to documentation of these functions. The package also contains function coclusterStrategy (see documentation of function to know various slots) which returns an object of class strategy. This object can be given as input to co-clustering functions to control various Co-clustering parameters. Please refer to testmodels.R file which is included in "test" directory to see examples with various models and simulated data-sets.

The package also provide utility functions like summary() and plot() to summarize results and plot the original and Co-clustered data respectively.

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)


## 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)

Simulated categorical Data-set

Description

It is a categorical data-set simulated using Categorical distribution with 5 modalities. It consist of three clusters in rows and two clusters in columns.

Format

A data matrix with 1000 rows and 100 columns.

Examples

  data(categoricaldata)
data(categoricaldata)

Categorical input/output options

Description

This class contains all the input options as well as the estimated paramters for categorical data-set. It inherits from base class CommonOptions. The class contains following output parameters given in 'Details' along with the parameters in base class.

Details

classmean:: The categorical distribution of each co-cluster
ICLvalue:: Integrated complete likelihood

Co-Clustering function.

Description

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

Usage

cocluster(
  data,
  datatype,
  semisupervised = FALSE,
  rowlabels = integer(0),
  collabels = integer(0),
  model = NULL,
  nbcocluster,
  strategy = coclusterStrategy(),
  nbCore = 1
)
cocluster(
  data,
  datatype,
  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, numeric vector for row effects and numeric vector column effects in case of contingency data with known row and column effects.)

datatype

This is the type of data which can be "binary" , "contingency", "continuous" or "categorical".

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 various types of 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
pik_rhol_sigma2kl(Default)	continuous	unequal	unequal
pik_rhol_sigma	continuous	unequal	equal
pi_rho_sigma2kl	continuous	equal	unequal
pi_rho_sigma2	continuous	equal	equal
pik_rhol_unknown(default)	contingency	unequal	N.A
pi_rho_unknown	contingency	equal	N.A
pik_rhol_known	contingency	unequal	N.A
pi_rho_known	contingency	equal	N.A
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.

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

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

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
)
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)

## 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)

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
)
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)

## 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)

Co-Clustering function.

Description

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

Usage

coclusterContingency(
  data,
  semisupervised = FALSE,
  rowlabels = integer(0),
  collabels = integer(0),
  model = NULL,
  nbcocluster,
  strategy = coclusterStrategy(),
  nbCore = 1
)
coclusterContingency(
  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, numeric vector for row effects and numeric vector column effects in case of contingency data with known row and column effects.)

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 Poisson data:

pik_rhol_unknown(default)	contingency	unequal	N.A
pi_rho_unknown	contingency	equal	N.A
pik_rhol_known	contingency	unequal	N.A
pi_rho_known	contingency	equal	N.A

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 contingency data
## load data
data(contingencydataunknown)
## usage of coclusterContingency function in its most simplest form
strategy = coclusterStrategy( nbinititerations = 5, nbxem = 2, nbiterations_int = 2
                            , nbiterationsxem = 10, nbiterationsXEM = 100, epsilonXEM=1e-5)
out<-coclusterContingency( contingencydataunknown, nbcocluster=c(2,3), strategy = strategy)
## Summarize the output results
summary(out)
## Plot the original and Co-clustered data 
plot(out)

## Simple example with simulated contingency data
## load data
data(contingencydataunknown)
## usage of coclusterContingency function in its most simplest form
strategy = coclusterStrategy( nbinititerations = 5, nbxem = 2, nbiterations_int = 2
                            , nbiterationsxem = 10, nbiterationsXEM = 100, epsilonXEM=1e-5)
out<-coclusterContingency( contingencydataunknown, nbcocluster=c(2,3), strategy = strategy)
## Summarize the output results
summary(out)
## Plot the original and Co-clustered data 
plot(out)

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
)
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)


# 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)

Strategy function

Description

This function is used to set all the parameters for Co-clustering. It returns an object of class strategy which can be given as input to coclusterBinary, coclusterCategorical, coclusterContingency, coclusterContinuous function.

This class contains all the input parameters to run coclustering.

Usage

coclusterStrategy(
  algo = "BEM",
  initmethod = "emInitStep",
  stopcriteria = "Parameter",
  nbiterationsxem = 50,
  nbiterationsXEM = 500,
  nbinitmax = 100,
  nbinititerations = 10,
  initepsilon = 0.01,
  nbiterations_int = 5,
  epsilon_int = 0.01,
  epsilonxem = 1e-04,
  epsilonXEM = 1e-10,
  nbtry = 2,
  nbxem = 5
)
coclusterStrategy(
  algo = "BEM",
  initmethod = "emInitStep",
  stopcriteria = "Parameter",
  nbiterationsxem = 50,
  nbiterationsXEM = 500,
  nbinitmax = 100,
  nbinititerations = 10,
  initepsilon = 0.01,
  nbiterations_int = 5,
  epsilon_int = 0.01,
  epsilonxem = 1e-04,
  epsilonXEM = 1e-10,
  nbtry = 2,
  nbxem = 5
)

Arguments

`algo`	The valid values for this parameter are "BEM" (Default), "BCEM", "BSEM" and "BGibbs" (only for Binary model).
`initmethod`	Method to initialize model parameters. The valid values are "cemInitStep", "emInitStep" and "randomInit".
`stopcriteria`	It specifies the stopping criteria. It can be based on either relative change in parameters (preffered due to computation reasons) value or relative change in pseudo log-likelihood. Valid criterion values are "Parameter" and "Likelihood". Default criteria is "Parameter".
`nbiterationsxem`	Number of EM iterations used during xem step. Default value is 50.
`nbiterationsXEM`	Number of EM iterations used during XEM step. Default value is 500.
`nbinitmax`	Maximal number initialization to try. Default value is 100.
`nbinititerations`	Number of Global iterations used in initialization step. Default value is 10.
`initepsilon`	Tolerance value used while initialization. Default value is 1e-2.
`nbiterations_int`	Number of iterations for internal E step. Default value is 5.
`epsilon_int`	Tolerance value for relative change in Parameter/likelihood for internal E-step. Default value is 1e-2.
`epsilonxem`	Tolerance value used during xem step. Default value is 1e-4.
`epsilonXEM`	Tolerance value used during XEM step. Default value is 1e-10
`nbtry`	Number of tries (XEM steps). Default value is 2.
`nbxem`	Number of xem steps. Default value is 5.

Details

algo:: Algorithm to be use for co-clustering.
stopcriteria:: Stopping criteria used to stop the algorithm.
initmethod:: Method to initialize model parameters.
nbinitmax:: Maximal number of initialization to try (if reached estimation failed)
nbinititerations:: Number of global iterations while running initialization.
initepsilon:: Tolerance value used while initialization.
nbiterations_int:: Number of iterations for internal E-step.
epsilon_int:: Tolerance value for internal E-step.
nbtry:: Number of tries.
nbxem:: Number of xem iterations.
nbiterationsxem:: Number of EM iterations used during xem.
nbiterationsXEM:: Number of EM iterations used during XEM.
epsilonxem:: Tolerance value used during xem.
epsilonXEM:: Tolerance value used during XEM.

Value

Object of class strategy

Examples

#Default strategy values

strategy<-coclusterStrategy()
summary(strategy)

#Default strategy values

strategy<-coclusterStrategy()
summary(strategy)

Common Input/Output options.

Description

This class contains all the input options and common output options for all kinds of data-sets (Binary, Categorical, Contingency and Continuous).

Details

The following are the various input options:
data:: Input data.
datatype:: Type of data.
semisupervised:: Boolean value specifying if Co-clustering is semi-supervised or not.
model:: Model to be run for co-clustering.
nbcocluster:: Number of row and column clusters.
strategy:: Input strategy.
The following are the various common output options:
message:: Status returned.
rowproportions:: Vector of row proportions.
colproportions:: Vector of column proportions.
rowclass:: Vector of assigned row cluster to each row.
colclass:: Vector of assigned column cluster to each column.
likelihood:: Final pseudo log-likelihood.
rowposteriorprob:: Final posterior probabilities for rows.
colposteriorprob:: Final posterior probabilities for columns.

Simulated Contingency Data-set

Description

It is a contingency data-set simulated using Poisson distribution. The row and column effects is unknown for this data-set. It consist of two clusters in rows and three clusters in columns.

Format

A data list consisting of following data:

data: A data matrix consisting of 1000 rows and 100 columns.
roweffects: A numeric vector of size 1000. Each value represent row effect of corresponding row.
columneffects: A numeric vector of size 100. Each value represent column effect of corresponding column.

Examples

  data(contingencydatalist)
data(contingencydatalist)

Simulated Contingency Data-set

Description

It is a contingency data-set simulated using Poisson distribution. The row and column effects is unknown for this data-set. It consist of two clusters in rows and three clusters in columns.

Format

A data matrix with 1000 rows and 100 columns.

Examples

  data(contingencydataunknown)
data(contingencydataunknown)

Contingency input/output options

Description

This class contains all the input options as well as the estimated paramters for Contingency data-set.It inherits from base class CommonOptions. The class contains following output parameters given in 'Details' along with the parameters in base class.

Details

classgamma:: The value of poisson parameter (gamma) for each co-cluster.
datamui:: Rows effect (if known).
datanuj:: Columns effect (if known).

Continuous input/output options

Description

This class contains all the input options as well as the estimated parameters for Continuous data-sets. It inherits from base class CommonOptions. The class contains following output parameters given in 'Details' along with the parameters in base class.

Details

classmean:: The mean value of each co-cluster.
classvariance:: The variance of each co-cluster.

Simulated Gaussian Data-set

Description

It is a Continuous data-set simulated using Gaussian distribution. It consist of two clusters in rows and three clusters in columns.

Format

A data matrix with 1000 rows and 100 columns.

Examples

  data(gaussiandata)
data(gaussiandata)

Plot function.

Description

This function plot the original and Co-clustered data-sets.

Usage

## S4 method for signature 'BinaryOptions'
plot(x, y, ...)

## S4 method for signature 'ContingencyOptions'
plot(x, y, ...)

## S4 method for signature 'ContinuousOptions'
plot(x, y, ...)

## S4 method for signature 'CategoricalOptions'
plot(x, y, ...)
## S4 method for signature 'BinaryOptions'
plot(x, y, ...)

## S4 method for signature 'ContingencyOptions'
plot(x, y, ...)

## S4 method for signature 'ContinuousOptions'
plot(x, y, ...)

## S4 method for signature 'CategoricalOptions'
plot(x, y, ...)

Arguments

`x`	output object from `coclusterBinary`, `coclusterCategorical`, `coclusterContingency`, `coclusterContinuous`.
`y`	Ignored
`...`	Additional argument(s). Currently we support two additional argument. "asp": If this is set to TRUE the original aspect ratio is conserved. By default "asp" is FALSE. "type" : This is the type of plot which is either "cocluster" or "distribution". The corresponding plots are Co-clustered data and distributions and mixture densities for Co-clusters respectively. Default is "cocluster" plot.

Summary function.

Description

This function gives the summary of output from coclusterBinary, coclusterCategorical, coclusterContingency, coclusterContinuous.

Usage

## S4 method for signature 'strategy'
summary(object, ...)

## S4 method for signature 'BinaryOptions'
summary(object, ...)

## S4 method for signature 'ContingencyOptions'
summary(object, ...)

## S4 method for signature 'ContinuousOptions'
summary(object, ...)

## S4 method for signature 'CategoricalOptions'
summary(object, ...)
## S4 method for signature 'strategy'
summary(object, ...)

## S4 method for signature 'BinaryOptions'
summary(object, ...)

## S4 method for signature 'ContingencyOptions'
summary(object, ...)

## S4 method for signature 'ContinuousOptions'
summary(object, ...)

## S4 method for signature 'CategoricalOptions'
summary(object, ...)

Arguments

`object`	output object from `coclusterBinary`, `coclusterCategorical`, `coclusterContingency`, `coclusterContinuous`.
`...`	Additional argument(s) . Currently there is no additional arguments.

An EM strategy to obtain a good optimum.

Description

In Co-clustering, there could be many local optimal where the algorithm may get struck resulting in sub-optimum results. Hence we applied a strategy called XEM strategy to run the EM algorithm. The various steps are defined as follows:

Details

Step-1, "xem" step:: Do several runs of: "initialization followed by short run of algorithm (few iterations/high tolerance)". This parameter is named as "nbxem" in coclusterStrategy function. Default value is 5. We call this step as xem step.
Step-2, "XEM" step:: Select the best result of step 1 and make long run of Algorithm(high iterations/low tolerance).We call this step as XEM step.
Step-3: Repeat step 1 and 2 several times and select the best result. The number of repetitions can be modified via parameter "nbtry" of coclusterStrategy function. Default value is 2.

Package 'blockcluster'

Help Index

Getter method for blockcluster output

Description

Usage

Arguments

Simulated Binary Data-set

Description

Format

Examples

Binary input/output options

Description

Details

Co-Clustering Package

Description

Details

Examples

Simulated categorical Data-set

Description

Format

Examples

Categorical input/output options

Description

Details

Co-Clustering function.

Description

Usage

Arguments

Value

Examples

Co-Clustering function for Binary data.

Description

Usage

Arguments

Value

Examples

Co-Clustering function for categorical data-sets.

Description

Usage

Arguments

Value

Examples

Co-Clustering function.

Description

Usage

Arguments

Value

Examples

Co-Clustering function.

Description

Usage

Arguments

Value

Examples

Strategy function

Description

Usage

Arguments

Details

Value

Examples

Common Input/Output options.

Description

Details

Simulated Contingency Data-set

Description

Format

Examples

Simulated Contingency Data-set

Description

Format

Examples

Contingency input/output options

Description

Details

Continuous input/output options

Description

Details

Simulated Gaussian Data-set

Description