#' initialization of the EM algorithm #' #' @param k number of components #' @param X matrix of covariates (of size n*p) #' @param Y matrix of responses (of size n*m) #' @param tau threshold to stop EM algorithm #' #' @return a list with phiInit, rhoInit, piInit, gamInit #' @export 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 require(mclust) # K-means with selection of K for(repet in 1:20) { clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean Zinit1[,repet] = clusters$classification for(r in 1:k) { Z = Zinit1[,repet] Z_bin = vec_bin(Z,r) Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r Z_indice = Z_bin$indice #renvoit les indices o? Z==r betaInit1[,,r,repet] = ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,] sigmaInit1[,,r,repet] = diag(m) phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet] rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) piInit1[repet,r] = sum(Z_vec)/n } for(i in 1:n) { for(r in 1:k) { dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) } sumGamI = sum(gam[i,]) gamInit1[i,,repet]= Gam[i,] / sumGamI } miniInit = 10 maxiInit = 11 new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau) LLFEessai = new_EMG$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)) }