This function implements the variable selection in discriminant analysis using a lasso ranking on the variables as described in Sedki et al (2014). The variable ranking step uses the penalized EM algorithm of Zhou et al (2009) (adapted in Sedki et al (2014) for the discriminant analysis settings). A testing sample can be used to compute the averaged classification error rate.
SelvarLearnLasso(x, z, lambda, rho, type, rank, hsize, models,
rmodel, imodel, xtest, ztest, nbcores)matrix containing quantitative data. Rows correspond to observations and columns correspond to variables
an integer vector or a factor corresponding to labels of data.
numeric listing of tuning parameters for \(\ell_1\) mean penalty
numeric listing of tuning parameters for \(\ell_1\) precision matrix penalty
character defining the type of ranking procedure, must be "lasso" or "likelihood". Default is "lasso"
integer listing the rank of variables with (the length of this vector must be equal to the number of variables in the dataset)
optional parameter make less strength the forward and backward algorithms to select \(S\) and \(W\) sets
list of character defining the covariance matrix form for the linear regression of \(U\) on the \(R\) set of variable. Possible values: "LI" for spherical form, "LB" for diagonal form and "LC" for general form. Possible values: "LI", "LB", "LC", c("LI", "LB") , c("LI", "LC"), c("LB", "LC") and c("LI", "LB", "LC"). Default is c("LI", "LB", "LC")
list of character defining the covariance matrix form for independent variables \(W\). Possible values: "LI" for spherical form and "LB" for diagonal form. Possible values: "LI", "LB", c("LI", "LB"). Default is c("LI", LB")
matrix containing quantitative testing data. Rows correspond to observations and columns correspond to variables
an integer vector or a factor of size number of testing observations. Each cell corresponds to a cluster affectation
number of CPUs to be used when parallel computing is used (default is 2)
The selected set of relevant clustering variables
The selected subset of regressors
The selected set of redundant variables
The selected set of independent variables
The criterion value for the selected model
The selected covariance model
The selected covariance form for the regression
The selected covariance form for the independent variables
Rmixmod ['>Parameter] object containing all mixture parameters
Matrix containing all regression coefficients, each column is the regression coefficients of one redundant variable on the selected R set
Optional : matrix containing the conditional probabilities of belonging to each cluster for the testing observations
Optional: vector containing the cluster assignments of the testing observations according to the Maximum-a-Posteriori rule. When testing dataset is missed, we use the training dataset as testing one
Optional : error rate done by the predicted partition (obtained using Maximum-A-Posteriori rule). When testing dataset is missed, we use the training dataset as testing one
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
# NOT RUN {
## wine data set
## n = 178 observations, p = 27 variables
data(wine)
set.seed(123)
a <- seq(1, 178, 10)
b <- setdiff(1:178, a)
obj <- SelvarLearnLasso(x=wine[b,1:27], z=wine[b,28], xt=wine[a,1:27], zt=wine[a,28], nbcores=4)
summary(obj)
print(obj)
# }
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