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) | |
6 | #' @param tau threshold to stop EM algorithm | |
7 | #' | |
8 | #' @return a list with phiInit, rhoInit, piInit, gamInit | |
9 | #' @export | |
39046da6 BA |
10 | initSmallEM = function(k,X,Y,tau) |
11 | { | |
d1531659 | 12 | n = nrow(Y) |
13 | m = ncol(Y) | |
14 | p = ncol(X) | |
15 | ||
16 | betaInit1 = array(0, dim=c(p,m,k,20)) | |
17 | sigmaInit1 = array(0, dim = c(m,m,k,20)) | |
18 | phiInit1 = array(0, dim = c(p,m,k,20)) | |
19 | rhoInit1 = array(0, dim = c(m,m,k,20)) | |
20 | piInit1 = matrix(0,20,k) | |
21 | gamInit1 = array(0, dim=c(n,k,20)) | |
22 | LLFinit1 = list() | |
23 | ||
24 | require(MASS) #Moore-Penrose generalized inverse of matrix | |
25 | for(repet in 1:20) | |
26 | { | |
27 | clusters = hclust(dist(y)) #default distance : euclidean | |
28 | #cutree retourne les indices (? quel cluster indiv_i appartient) d'un clustering hierarchique | |
29 | clusterCut = cutree(clusters,k) | |
30 | Zinit1[,repet] = clusterCut | |
31 | ||
32 | for(r in 1:k) | |
33 | { | |
34 | Z = Zinit1[,repet] | |
35 | Z_bin = vec_bin(Z,r) | |
36 | Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r | |
37 | Z_indice = Z_bin$indice #renvoit les indices o? Z==r | |
38 | ||
39 | betaInit1[,,r,repet] = | |
40 | 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 = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% | |
52 | (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) | |
53 | Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) | |
54 | } | |
55 | sumGamI = sum(gam[i,]) | |
56 | gamInit1[i,,repet]= Gam[i,] / sumGamI | |
57 | } | |
58 | ||
59 | miniInit = 10 | |
60 | maxiInit = 11 | |
61 | ||
62 | new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], | |
63 | gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau) | |
64 | LLFEessai = new_EMG$LLF | |
65 | LLFinit1[repet] = LLFEessai[length(LLFEessai)] | |
66 | } | |
67 | ||
68 | b = which.max(LLFinit1) | |
69 | phiInit = phiInit1[,,,b] | |
70 | rhoInit = rhoInit1[,,,b] | |
71 | piInit = piInit1[b,] | |
72 | gamInit = gamInit1[,,b] | |
73 | ||
74 | return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) | |
39046da6 | 75 | } |