Commit | Line | Data |
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d1531659 | 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) | |
d1531659 | 6 | #' |
7 | #' @return a list with phiInit, rhoInit, piInit, gamInit | |
8 | #' @export | |
e3f2fe8a | 9 | #' @importFrom methods new |
10 | #' @importFrom stats cutree dist hclust runif | |
aa480ac1 | 11 | initSmallEM = function(k,X,Y, fast=TRUE) |
39046da6 | 12 | { |
e166ed4e BA |
13 | n = nrow(Y) |
14 | m = ncol(Y) | |
15 | p = ncol(X) | |
0930b5d3 | 16 | nIte = 20 |
4d9db27f | 17 | Zinit1 = array(0, dim=c(n,nIte)) |
18 | betaInit1 = array(0, dim=c(p,m,k,nIte)) | |
19 | sigmaInit1 = array(0, dim = c(m,m,k,nIte)) | |
20 | phiInit1 = array(0, dim = c(p,m,k,nIte)) | |
21 | rhoInit1 = array(0, dim = c(m,m,k,nIte)) | |
ae4fa2cb | 22 | Gam = matrix(0, n, k) |
4d9db27f | 23 | piInit1 = matrix(0,nIte,k) |
24 | gamInit1 = array(0, dim=c(n,k,nIte)) | |
e166ed4e BA |
25 | LLFinit1 = list() |
26 | ||
0eb161e3 | 27 | #require(MASS) #Moore-Penrose generalized inverse of matrix |
4d9db27f | 28 | for(repet in 1:nIte) |
e166ed4e | 29 | { |
4d9db27f | 30 | distance_clus = dist(cbind(X,Y)) |
4725af56 BG |
31 | tree_hier = hclust(distance_clus) |
32 | Zinit1[,repet] = cutree(tree_hier, k) | |
33 | ||
e166ed4e BA |
34 | for(r in 1:k) |
35 | { | |
36 | Z = Zinit1[,repet] | |
c3bc4705 | 37 | Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r |
e3f2fe8a | 38 | if (length(Z_indice) == 1) { |
0eb161e3 | 39 | betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*% |
e3f2fe8a | 40 | crossprod(t(X[Z_indice,]), Y[Z_indice,]) |
41 | } else { | |
0eb161e3 | 42 | betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*% |
ef67d338 | 43 | crossprod(X[Z_indice,], Y[Z_indice,]) |
e3f2fe8a | 44 | } |
e166ed4e | 45 | sigmaInit1[,,r,repet] = diag(m) |
4725af56 | 46 | phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet] |
e166ed4e | 47 | rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) |
c3bc4705 | 48 | piInit1[repet,r] = mean(Z == r) |
e166ed4e BA |
49 | } |
50 | ||
51 | for(i in 1:n) | |
52 | { | |
53 | for(r in 1:k) | |
54 | { | |
4725af56 | 55 | dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) |
e166ed4e BA |
56 | Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) |
57 | } | |
ae4fa2cb | 58 | sumGamI = sum(Gam[i,]) |
e166ed4e BA |
59 | gamInit1[i,,repet]= Gam[i,] / sumGamI |
60 | } | |
61 | ||
62 | miniInit = 10 | |
63 | maxiInit = 11 | |
64 | ||
4d9db27f | 65 | init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,], |
43d76c49 | 66 | gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast) |
4d9db27f | 67 | LLFEessai = init_EMG$LLF |
e166ed4e BA |
68 | LLFinit1[repet] = LLFEessai[length(LLFEessai)] |
69 | } | |
4d9db27f | 70 | b = which.min(LLFinit1) |
e166ed4e BA |
71 | phiInit = phiInit1[,,,b] |
72 | rhoInit = rhoInit1[,,,b] | |
73 | piInit = piInit1[b,] | |
74 | gamInit = gamInit1[,,b] | |
75 | ||
76 | return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) | |
39046da6 | 77 | } |