constructionModelesLassoMLE {valse}

constructionModelesLassoMLE

Description

Construct a collection of models with the Lasso-MLE procedure.

Usage

constructionModelesLassoMLE(
  phiInit,
  rhoInit,
  piInit,
  gamInit,
  mini,
  maxi,
  gamma,
  X,
  Y,
  eps,
  S,
  ncores,
  fast,
  verbose
)

Arguments

phiInit

an initialization for phi, get by initSmallEM.R

rhoInit

an initialization for rho, get by initSmallEM.R

piInit

an initialization for pi, get by initSmallEM.R

gamInit

an initialization for gam, get by initSmallEM.R

mini

integer, minimum number of iterations in the EM algorithm, by default = 10

maxi

integer, maximum number of iterations in the EM algorithm, by default = 100

gamma

integer for the power in the penaly, by default = 1

X

matrix of covariates (of size n*p)

Y

matrix of responses (of size n*m)

eps

real, threshold to say the EM algorithm converges, by default = 1e-4

S

output of selectVariables.R

ncores

Number of cores, by default = 3

fast

TRUE to use compiled C code, FALSE for R code only

verbose

TRUE to show some execution traces

Value

a list with several models, defined by phi (the regression parameter reparametrized), rho (the covariance parameter reparametrized), pi (the proportion parameter is the mixture model), llh (the value of the loglikelihood function for this estimator on the training dataset). The list is given for several levels of sparsity, given by several regularization parameters computed automatically.