vec_bin = function(X,r) { Z = c() indice = c() j = 1 for (i in 1:length(X)) { if(X[i] == r) { Z[i] = 1 indice[j] = i j=j+1 } else Z[i] = 0 } return (list(Z=Z,indice=indice)) } initSmallEM = function(k,X,Y,tau) { n = nrow(Y) m = ncol(Y) p = ncol(X) betaInit1 = array(0, dim=c(p,m,k,20)) sigmaInit1 = array(0, dim = c(m,m,k,20)) phiInit1 = array(0, dim = c(p,m,k,20)) rhoInit1 = array(0, dim = c(m,m,k,20)) piInit1 = matrix(0,20,k) gamInit1 = array(0, dim=c(n,k,20)) LLFinit1 = list() require(MASS) #Moore-Penrose generalized inverse of matrix for(repet in 1:20) { clusters = hclust(dist(y)) #default distance : euclidean #cutree retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique clusterCut = cutree(clusters,k) Zinit1[,repet] = clusterCut for(r in 1:k) { Z = Zinit1[,repet] Z_bin = vec_bin(Z,r) Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits où Z==r Z_indice = Z_bin$indice #renvoit les indices où Z==r betaInit1[,,r,repet] = ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,] sigmaInit1[,,r,repet] = diag(m) phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet] rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) piInit1[repet,r] = sum(Z_vec)/n } for(i in 1:n) { for(r in 1:k) { dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) } sumGamI = sum(gam[i,]) gamInit1[i,,repet]= Gam[i,] / sumGamI } miniInit = 10 maxiInit = 11 new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau) LLFEessai = new_EMG$LLF LLFinit1[repet] = LLFEessai[length(LLFEessai)] } b = which.max(LLFinit1) phiInit = phiInit1[,,,b] rhoInit = rhoInit1[,,,b] piInit = piInit1[b,] gamInit = gamInit1[,,b] return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) }