boscoclust: Function to perform a co-clustering

In ordinalClust: Ordinal Data Clustering, Co-Clustering and Classification

Function to perform a co-clustering

Description

This function runs a co-clustering algorithm on ordinal data by using the latent block model (see references for further details). A BOS distribution is used, and the parameters inference is obtained using the SEM-Gbbs algorithm.

Usage

boscoclust(x=matrix(0,nrow=1,ncol=1), idx_list=c(1), kr, kc, init, nbSEM, nbSEMburn, 
          nbRepeat=1, nbindmini, m=0, percentRandomB=0)

Arguments

x	Matrix made of ordinal data of dimension N*Jtot. The features with the same numbers of levels must be placed side by side. The missing values should be coded as NA.
idx_list	Vector of length D. This argument is useful when variables have different numbers of levels. Element d should indicate where the variables with number of levels m[d] begin in matrix x.
kr	Number of row classes.
kc	Vector of length D. The d-th element indicates the number of column clusters.
m	Vector of length D. The d-th element defines the number of levels of the ordinal data.
nbSEM	Number of SEM-Gibbs iterations realized to estimate parameters.
nbSEMburn	Number of SEM-Gibbs burn-in iterations for estimating parameters. This parameter must be inferior to nbSEM.
nbRepeat	Number of times sampling on rows and columns will be done for each SEM-Gibbs iteration.
nbindmini	Minimum number of cells belonging to a block.
init	String that indicates the kind of initialisation. Must be one of the following words : "kmeans", "random" or "randomBurnin".
percentRandomB	Vector of length 2. Indicates the percentage of resampling when init is equal to "randomBurnin".

Value

@V	Matrix of dimension N*kr such that V[i,g]=1 if i belongs to cluster g.
@icl	ICL value for co-clustering.
@name	Name of the result.
@paramschain	List of length nbSEMburn. The parameters of the blocks are stored for each iteration of the SEM-Gibbs algorithm.
@pichain	List of length nbSEM. Item i is a vector of length kr that contains the row mixing proportions at iteration i.
@rhochain	List of length nbSEM. Item i is a list of length D whose d-th element contains the column mixing proportions of the group of variables d, for iteration i.
@zc	List of length D. The d-th item is a vector of length J[d] representing the column partitions for the group of variables d.
@zr	Vector of length N with resulting row partitions.
@W	List of length D. Item d is a matrix of dimension J*kc[d] such that W[j,h]=1 if j belongs to cluster h.
@m	Vector of length D. The d-th element represents the number of levels of d-th group of variables.
@params	List of length D. The d-th item represents the blocks parameters for a group of variables d.
@pi	Vector of length kr. This corresponds to the row mixing proportions.
@rho	List of length D. The d-th item represents the column mixing proportion for the d-th group of variables.
@xhat	List of length D. The d-th item represents the dataset of the d-th group of variables, with missing values completed.
@zrchain	Matrix of dimension nbSEM*N. Row i represents the row cluster partitions at iteration i.
@zrchain	List of length D. Item d is a matrix of dimension nbSEM*J[d]. Row i represents the column cluster partitions at iteration i.

Author(s)

Margot Selosse, Julien Jacques, Christophe Biernacki.

Examples

  
  
    
  library(ordinalClust)

  # loading the real dataset
  data("dataqol")
  set.seed(5)

  # loading the ordinal data
  M <- as.matrix(dataqol[,2:29])


  # defining different number of categories:
  m=4


  # defining number of row and column clusters
  krow = 4
  kcol = 4

  # configuration for the inference
  nbSEM=50
  nbSEMburn=40
  nbindmini=2
  init = "randomBurnin"
  percentRandomB=c(20,20)



  # Co-clustering execution
  object <- boscoclust(x=M,kr=krow,kc=kcol,m=m,nbSEM=nbSEM,
            nbSEMburn=nbSEMburn, nbindmini=nbindmini, init=init, percentRandomB=percentRandomB)

  
  

ordinalClust documentation built on April 22, 2026, 1:07 a.m.