| 1 | #' initialization of the EM algorithm |
| 2 | #' |
| 3 | #' @param k number of components |
| 4 | #' @param X matrix of covariates (of size n*p) |
| 5 | #' @param Y matrix of responses (of size n*m) |
| 6 | #' @param tau threshold to stop EM algorithm |
| 7 | #' |
| 8 | #' @return a list with phiInit, rhoInit, piInit, gamInit |
| 9 | #' @export |
| 10 | initSmallEM = function(k,X,Y,tau) |
| 11 | { |
| 12 | n = nrow(Y) |
| 13 | m = ncol(Y) |
| 14 | p = ncol(X) |
| 15 | |
| 16 | Zinit1 = array(0, dim=c(n,20)) #doute sur la taille |
| 17 | betaInit1 = array(0, dim=c(p,m,k,20)) |
| 18 | sigmaInit1 = array(0, dim = c(m,m,k,20)) |
| 19 | phiInit1 = array(0, dim = c(p,m,k,20)) |
| 20 | rhoInit1 = array(0, dim = c(m,m,k,20)) |
| 21 | Gam = matrix(0, n, k) |
| 22 | piInit1 = matrix(0,20,k) |
| 23 | gamInit1 = array(0, dim=c(n,k,20)) |
| 24 | LLFinit1 = list() |
| 25 | |
| 26 | require(MASS) #Moore-Penrose generalized inverse of matrix |
| 27 | require(mclust) # K-means with selection of K |
| 28 | for(repet in 1:20) |
| 29 | { |
| 30 | clusters = Mclust(X,k) #default distance : euclidean #Mclust(matrix(c(X,Y)),k) |
| 31 | Zinit1[,repet] = clusters$classification |
| 32 | |
| 33 | for(r in 1:k) |
| 34 | { |
| 35 | Z = Zinit1[,repet] |
| 36 | Z_bin = vec_bin(Z,r) |
| 37 | Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r |
| 38 | Z_indice = Z_bin$indice #renvoit les indices o? Z==r |
| 39 | |
| 40 | betaInit1[,,r,repet] = ginv(t(X[Z_indice,])%*%X[Z_indice,])%*%t(X[Z_indice,])%*%Y[Z_indice,] |
| 41 | sigmaInit1[,,r,repet] = diag(m) |
| 42 | phiInit1[,,r,repet] = betaInit1[,,r,repet]#/sigmaInit1[,,r,repet] |
| 43 | rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) |
| 44 | piInit1[repet,r] = sum(Z_vec)/n |
| 45 | } |
| 46 | |
| 47 | for(i in 1:n) |
| 48 | { |
| 49 | for(r in 1:k) |
| 50 | { |
| 51 | dotProduct = 3 #(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) %*% (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) |
| 52 | Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) |
| 53 | } |
| 54 | sumGamI = sum(Gam[i,]) |
| 55 | gamInit1[i,,repet]= Gam[i,] / sumGamI |
| 56 | } |
| 57 | |
| 58 | miniInit = 10 |
| 59 | maxiInit = 11 |
| 60 | |
| 61 | new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], |
| 62 | gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau) |
| 63 | LLFEessai = new_EMG$LLF |
| 64 | LLFinit1[repet] = LLFEessai[length(LLFEessai)] |
| 65 | } |
| 66 | |
| 67 | b = which.max(LLFinit1) |
| 68 | phiInit = phiInit1[,,,b] |
| 69 | rhoInit = rhoInit1[,,,b] |
| 70 | piInit = piInit1[b,] |
| 71 | gamInit = gamInit1[,,b] |
| 72 | |
| 73 | return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) |
| 74 | } |