X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=src%2Ftest%2Fgenerate_test_data%2Fhelpers%2FconstructionModelesLassoRank.R;h=ad4f7258b4d27ab6ae88c6fc99626e7e55b269d5;hp=183a6d168fba2f98ddfc9af3421e5222cf86977b;hb=c3bc47052f3ccb659659c59a82e9a99ea842398d;hpb=e39bc178cf5de02489ea2dce3869ba6323e18492

diff --git a/src/test/generate_test_data/helpers/constructionModelesLassoRank.R b/src/test/generate_test_data/helpers/constructionModelesLassoRank.R
index 183a6d1..ad4f725 100644
--- a/src/test/generate_test_data/helpers/constructionModelesLassoRank.R
+++ b/src/test/generate_test_data/helpers/constructionModelesLassoRank.R
@@ -14,7 +14,7 @@ constructionModelesLassoRank = function(Pi,Rho,mini,maxi,X,Y,tau,A1,rangmin,rang
 #  }
   
   phi = array(0, dim=c(p,m,k,L*Size))
-  lvraisemblance = matrix(0, L*Size, 2)
+  llh = matrix(0, L*Size, 2) #log-likelihood
   for(lambdaIndex in 1:L){
     #on ne garde que les colonnes actives
     #active sera l'ensemble des variables informatives
@@ -25,10 +25,10 @@ constructionModelesLassoRank = function(Pi,Rho,mini,maxi,X,Y,tau,A1,rangmin,rang
         EMG_rank = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi, X[, active], Y, tau, Rank[j,])
         phiLambda = EMG_rank$phi
         LLF = EMG_rank$LLF
-        lvraisemblance[(lambdaIndex-1)*Size+j,] = c(LLF, sum(Rank[j,]^(length(active)- Rank[j,]+m)))
+        llh[(lambdaIndex-1)*Size+j,] = c(LLF, sum(Rank[j,]^(length(active)- Rank[j,]+m)))
         phi[active,,,(lambdaIndex-1)*Size+j] = phiLambda
       }
     }
   }
-  return(list(phi=phi, lvraisemblance = lvraisemblance))
+  return(list(phi=phi, llh = llh))
 }