Package {mixedClust}

Type: Package
Title: Co-Clustering of Mixed Type Data
Version: 1.0.2.1
Date: 2026-06-01
Maintainer: Julien Jacques <julien.jacques@univ-lyon2.fr>
Description: Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) https://hal.science/hal-01893457. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 0.12.11), fda, methods
LinkingTo: Rcpp, RcppProgress, RcppArmadillo
Suggests: rmarkdown, ordinalClust, knitr
VignetteBuilder: knitr
LazyData: true
Depends: R (≥ 3.5.0)
NeedsCompilation: yes
Packaged: 2026-06-09 15:31:14 UTC; siyanasran
Repository: CRAN
Date/Publication: 2026-06-17 13:30:08 UTC
Author: Margot Selosse [aut], Julien Jacques [aut, cre], Christophe Biernacki [aut]

Matrix of simulated ordinal data

Description

This is a toy dataset for running simple examples.

Usage

M1

Format

A mixed type data matrix with 50 lines and 120 columns. There are 40 categorical variables, 40 continuous variables, and 40 ordinal variables.

Function to perform a co-clustering

Description

This function performs a co-clustering on heterogeneous data sets by using the Multiple Latent Block model (cf references for further details).

Usage

mixedCoclust(x=matrix(0,nrow=1,ncol=1), idx_list=c(1), distrib_names,
          kr, kc, init, nbSEM, nbSEMburn, nbRepeat=1, nbindmini, m=0, 
          functionalData=array(0, c(1,1,1)), zrinit= 0 , zcinit=0, 
          percentRandomB=0, percentRandomP=0)

Arguments

x

Data matrix, of dimension N*Jtot. The features with same type should be aside. The missing values should be coded as NA.

idx_list

Vector of length D. This argument is useful when variables are of different types. Element d should indicate where the variables of type d begins in matrix x.

distrib_names

Vector of length D. indicates the type of distribution to use. Must be among "Gaussian", "Multinomial", "BOS", "Poisson" or "Functional". Functional data must always be at the end.

kr

Number of row classes.

kc

Vector of length D. d-th element indicates the number of column clusters.

m

Vector of length D. d-th element defines the ordinal and categorical data's number of levels.

functionalData

Data tensor of dimension N*J*T.

nbSEM

Number of SEM-Gibbs iterations realized to estimate parameters.

nbSEMburn

Number of SEM-Gibbs burning iterations for estimating parameters. This parameter must be inferior to nbSEM.

nbRepeat

Number of times sampling on rows and on colums will be done at 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 th following words : "kmeans", "random", "provided", "randomParams" or "randomBurnin".

zrinit

Vector of length N. When init="provided", indicates the labels of each row.

zcinit

Vector of length Jtot. When init="provided", indicates the labels of each column.

percentRandomB

Vector of length 2. Indicates the percentage of resampling when init is equal to "randomBurnin".

percentRandomP

Vector of length 2. Indicates the percentage of resampling when init is equal to "randomParams".

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
@paramschain

List of length nbSEMburn. For each iteration of the SEM-Gibbs algorithm, the parameters of the blocks are stored.

@pichain

List of length nbSEM. Item i is a vector of length kr which contains the row mixing proportions at iteration i.

@rhochain

List of length nbSEM. Item i is a list of length D whose d-th contains the column mixing proportions of groups of variables d, at iteration i.

@zc

List of length D. d-th item is a vector of length J[d] representing the columns 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. d-th element represents the number of levels of d-th group of variables.

@params

List of length D. d-th item represents the blocks paramaters for group of variables d.

@pi

Vector of length kr. Row mixing proportions.

@rho

List of length D. d-th item represents the column mixing proportion for d-th group of variables.

@xhat

List of length D. d-th item represents the d-th group of variables dataset, 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

  
    data(M1)
    nbSEM=30
    nbSEMburn=20
    nbindmini=1
    init = "random"

    kr=2
    kc=c(2,2,2)
    m=c(6,3)
    d.list <- c(1,41,81)
    distributions <- c("Multinomial","Gaussian","Bos")
    res <- mixedCoclust(x = M1, idx_list = d.list,distrib_names = distributions,
                        kr = kr, kc = kc, m = m, init = init,nbSEM = nbSEM,
                        nbSEMburn = nbSEMburn, nbindmini = nbindmini)