SortvarLearn: Variable ranking with LASSO in discriminant analysis

Description

This function implements variable ranking procedure in discriminant analysis using the penalized EM algorithm of Zhou et al (2009) (adapted in Sedki et al (2014) for the discriminant analysis settings).

Usage

SortvarLearn(x, z, type, lambda, rho, nbcores)

Arguments

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

an integer vector or a factor corresponding to labels of data.

type

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

lambda

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

rho

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

nbcores

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

Value

vector of integers corresponding to variable ranking.

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 <- SortvarLearn(x=wine[,1:27], z=wine[,28], nbcores=4)
# }