Run a generalized EM algorithm developped for mixture of Gaussian regression models with variable selection by an extension of the Lasso estimator (regularization parameter lambda). Reparametrization is done to ensure invariance by homothetic transformation. It returns a collection of models, varying the number of clusters and the sparsity in the regression mean.
EMGLLF(
phiInit,
rhoInit,
piInit,
gamInit,
mini,
maxi,
gamma,
lambda,
X,
Y,
eps,
fast
)an initialization for phi
an initialization for rho
an initialization for pi
initialization for the a posteriori probabilities
integer, minimum number of iterations in the EM algorithm, by default = 10
integer, maximum number of iterations in the EM algorithm, by default = 100
integer for the power in the penaly, by default = 1
regularization parameter in the Lasso estimation
matrix of covariates (of size n*p)
matrix of responses (of size n*m)
real, threshold to say the EM algorithm converges, by default = 1e-4
boolean to enable or not the C function call
A list (corresponding to the model collection) defined by (phi,rho,pi,llh,S,affec): phi : regression mean for each cluster, an array of size p*m*k rho : variance (homothetic) for each cluster, an array of size m*m*k pi : proportion for each cluster, a vector of size k llh : log likelihood with respect to the training set S : selected variables indexes, an array of size p*m*k affec : cluster affectation for each observation (of the training set)