constructionModelesLassoRank: constructionModelesLassoRank

In valse: Variable Selection with Mixture of Models

Description

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

Usage

constructionModelesLassoRank(
  S,
  k,
  mini,
  maxi,
  X,
  Y,
  eps,
  rank.min,
  rank.max,
  ncores,
  fast,
  verbose
)

Arguments

S	output of selectVariables.R
k	number of components
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
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
rank.min	integer, minimum rank in the low rank procedure, by default = 1
rank.max	integer, maximum rank in the low rank procedure, by default = 5
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, and several ranks (between rank.min and rank.max).

valse documentation built on May 31, 2021, 9:10 a.m.