# }
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
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))
}