-#' Discard models which have the same relevant variables - for EMGLLF
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param B2 array of irrelevant coefficients (of size p*m*length(gridlambda))
-#' @param glambda grid of regularization parameters (vector)
-#' @param rho covariance matrix (of size m*m*K*size(gridLambda))
-#' @param pi weight parameters (of size K*size(gridLambda))
-#'
-#' @return a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
-#' of selected models.
-#' @export
-discardSimilarModels_EMGLLF = function(B1,B2,glambda,rho,pi)
-{
- ind = c()
- for (j in 1:length(glambda))
- {
- for (ll in 1:(l-1))
- {
- if(B1[,,l] == B1[,,ll])
- ind = c(ind, l)
- }
- }
- ind = unique(ind)
- B1 = B1[,,-ind]
- glambda = glambda[-ind]
- B2 = B2[,,-ind]
- rho = rho[,,,-ind]
- pi = pi[,-ind]
-
- return (list("B1"=B1,"B2"=B2,"glambda"=glambda,"rho"=rho,"pi"=pi,"ind"=ind))
-}
-
-#' Discard models which have the same relevant variables
-#' - for Lasso-rank procedure (focus on columns)
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param rho covariance matrix
-#' @param pi weight parameters
-#'
-#' @return a list with B1, in, rho, pi
-#' @export
-discardSimilarModels_EMGrank = function(B1,rho,pi)
-{
- ind = c()
- dim_B1 = dim(B1)
- B2 = array(0,dim=c(dim_B1[1],dim_B1[2],dim_B1[3]))
- sizeLambda=dim_B1[3]
- glambda = rep(0,sizeLambda)
-
- suppressmodel = discardSimilarModels_EMGLLF(B1,B2,glambda,rho,pi)
- return (list("B1" = suppressmodel$B1, "ind" = suppressmodel$ind,
- "rho" = suppressmodel$rho, "pi" = suppressmodel$pi))
-}