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(data, knownlabels, lambda, rho, nbCores)
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data |
matrix containing quantitative data. Rows correspond to observations and columns correspond to variables |
knownlabels |
an integer vector or a factor of size number of observations. Each cell corresponds to a cluster affectation. So the maximum value is the number of clusters. |
lambda |
numeric listing of tuning parameter for \ell_1 mean penalty |
rho |
numeric listing of tuning parameter 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## 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]
labels.cor <-scenarioCor[,15]
lambda <- seq(20, 50, length = 10)
rho <- seq(1, 2, length=2)
## variable ranking in discriminant analysis
var.ranking.da <- SortvarLearn(data.cor, labels.cor, lambda, rho)
## End(Not run)
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