Description Usage Arguments Value Author(s) References See Also Examples
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).
1 | SortvarLearn(x, z, type, lambda, rho, nbcores)
|
x |
matrix containing quantitative data. Rows correspond to observations and columns correspond to variables |
z |
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) |
vector of integers corresponding to variable ranking.
Mohammed Sedki mohammed.sedki@u-psud.fr
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
SortvarClust
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Loading required package: glasso
Loading required package: Rmixmod
Loading required package: Rcpp
Rmixmod v. 2.1.2.2 / URI: www.mixmod.org
Loading required package: parallel
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