README.md

In MoMPCA: Inference and Clustering for Mixture of Multinomial Principal Component Analysis

Cluster any count data matrix with a fixed number of variables. Implements the branch & bound Classification-Variational Expectation-Maximisation of this paper (to appear in Computational Statistics).

Installation

MoMPCA is available on CRAN and the development version available on Github.

R Package installation

CRAN dependencies

MoMPCA needs the following CRAN R packages, so check that they are are installed on your computer.

required_CRAN <- c("methods",
    "topicmodels",
    "tm",
    "Matrix",
    "slam",
    "magrittr",
    "dplyr",
    "stats",
    "doParallel",
    "foreach", 
    "ggplot2",
    "reshape2",
    "tidytext")
not_installed_CRAN <- setdiff(required_CRAN, rownames(installed.packages()))
if (length(not_installed_CRAN) > 0) install.packages(not_installed_CRAN)

Installing MoMPCA

• For the last stable version, use the CRAN version

install.packages("MoMPCA")

• For the development version, use the github install

remotes::install_github("nicolasJouvin/MoMPCA")

Usage and main fitting functions

The package comes with the BBCmsg data set and a simulate_BBC() function wich allows to reproduce the simulation of the paper.

library(MoMPCA)
simu <- simulate_BBC(N = 400, L = 200, epsilon = 0, lambda = 1)
dtm <- simu$dtm.full
Ytruth <- simu$Ytruth # true clustering

The dtm is a tm::DocumentTermMatrix() object. The main fitting function is mmpca_clust(), which allow for a parralel backend via its argument mc.cores. There is a simple wrapper around this function called mmpca_clust_modelselect() which allows for model selection of (Q, K) with an ICL criterion. Please be aware that the greedy nature of the algorithm may induce quite intensive computations.

Greedy clustering with a mixture of multinomial PCA

res <- mmpca_clust(simu$dtm.full, Q = 6, K = 4,
                          Yinit = 'random',
                          method = 'BBCVEM',
                          max.epochs = 7,
                          keep = 1,
                          verbose = 2,
                          nruns = 2,
                          mc.cores = 1)

The top words of the topic matrix beta can then be plotted (if working with text)

ggtopics <- plot(res, type = 'topics', n_words = 5)
print(ggtopics)

And the bound evolution throughout the epochs

ggbound <- plot(res, type = 'bound')
print(ggbound)

Model selection

res <- mmpca_clust_modelselect(simu$dtm.full, Qs = 5:7, Ks = 3:5,
                          Yinit = 'kmeans_lda',
                          init.beta = 'lda',
                          method = 'BBCVEM',
                          max.epochs = 7,
                          nruns = 3,
                          verbose = 1)


best_model = res$models

References

Please cite our work using the following reference:

• N. Jouvin, P. Latouche, C. Bouveyron, A. Livartowski, G. Bataillon, Greedy clustering of count data through a mixture of multinomial PCA (To appear in Computational Statistics)

@article{jouvin:hal-02278224,
  TITLE = {{Greedy clustering of count data through a mixture of multinomial PCA}},
  AUTHOR = {Jouvin, Nicolas and Latouche, Pierre and Bouveyron, Charles and Bataillon, Guillaume and Livartowski, Alain},
  URL = {https://hal.archives-ouvertes.fr/hal-02278224},
  NOTE = {31 pages, 10 figures},
  JOURNAL = {{Computational Statistics}},
  PUBLISHER = {{Springer Verlag}},
  YEAR = {2020},
  KEYWORDS = {Dimension reduction ; Topic modeling ; Count data ; Mixture models ; Clustering ; Variational inference},
  HAL_ID = {hal-02278224},
  HAL_VERSION = {v1},
}

and consider citing this package

citation('MoMPCA')
## 
## To cite package 'MoMPCA' in publications use:
## 
##   Nicolas Jouvin (2020). MoMPCA: Inference and Clustering for Mixture
##   of Multinomial Principal Component Analysis. R package version 1.0.0.
##   https://CRAN.R-project.org/package=MoMPCA
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {MoMPCA: Inference and Clustering for Mixture of Multinomial Principal
## Component Analysis},
##     author = {Nicolas Jouvin},
##     year = {2020},
##     note = {R package version 1.0.0},
##     url = {https://CRAN.R-project.org/package=MoMPCA},
##   }
## 
## ATTENTION: This citation information has been auto-generated from the
## package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.

MoMPCA documentation built on Jan. 21, 2021, 5:09 p.m.