Description Usage Arguments Value Author(s) References See Also Examples
This function implements variable ranking procedure in model-based clustering using the penalized EM algorithm of Zhou et al (2009).
1 | SortvarClust(x, nbcluster, type, lambda, rho, nbcores)
|
x |
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) |
matrix where rows correspond to variable ranking. Each row corresponds to a competing value of nbcluster.
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
SortvarLearn
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