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(data, nbCluster, lambda, rho, nbCores)
|
data |
matrix containing quantitative data. Rows correspond to observations and columns correspond to variables |
nbCluster |
numeric listing of the number of clusters (must be integers) |
lambda |
numeric listing of the tuning parameter for \ell_1 mean penalty |
rho |
numeric listing of the tuning parameter for \ell_1 precision matrix penalty |
nbCores |
number of CPUs to be used when parallel computing is utilized (default is 2) |
matrix with rows corresponding to variable ranking. Each row corresponds to a value 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
## Simulated data example as shown in Sedki et al (2014)
## n = 2000 observations, p = 14 variables
require(glasso)
data(scenarioCor)
data.cor <- scenarioCor[,1:14]
lambda <- seq(20, 100, by = 10)
rho <- seq(1, 2, length=2)
nbCluster <- c(3, 4)
## variable ranking in model-based clustering
var.ranking.cl <- SortvarClust(data.cor, nbCluster, lambda, rho)
## End(Not run)
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