SortvarClust: Variable ranking with LASSO in model-based clustering

Description

This function implements variable ranking procedure in model-based clustering using the penalized EM algorithm of Zhou et al (2009).

Usage

SortvarClust(x, nbcluster, type, lambda, rho, nbcores)

Arguments

matrix containing quantitative data. Rows correspond to observations and columns correspond to variables

nbcluster

numeric listing of the number of clusters (must be integers)

type

character defining the type of ranking procedure, must be "lasso" or "likelihood". Default is "lasso"

lambda

numeric listing of the tuning parameters for \(\ell_1\) mean penalty

rho

numeric listing of the tuning parameters for \(\ell_1\) precision matrix penalty

nbcores

number of CPUs to be used when parallel computing is utilized (default is 2)

Value

matrix where rows correspond to variable ranking. Each row corresponds to a competing value of nbcluster.

References

Zhou, H., Pan, W., and Shen, X., 2009. "Penalized model-based clustering with unconstrained covariance matrices". Electronic Journal of Statistics, vol. 3, pp.1473-1496.

Maugis, C., Celeux, G., and Martin-Magniette, M. L., 2009. "Variable selection in model-based clustering: A general variable role modeling". Computational Statistics and Data Analysis, vol. 53/11, pp. 3872-3882.

Sedki, M., Celeux, G., Maugis-Rabusseau, C., 2014. "SelvarMix: A R package for variable selection in model-based clustering and discriminant analysis with a regularization approach". Inria Research Report available at http://hal.inria.fr/hal-01053784

Examples

Run this code

# NOT RUN {
## wine data set 
## n = 178 observations, p = 27 variables 
require(Rmixmod)
require(glasso)
data(wine)
set.seed(123)
obj <- SortvarClust(x=wine[,1:27], nbcluster=1:5, nbcores=4)
# }