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 | |
11 | initSmallEM = function(k,X,Y) | |
39046da6 | 12 | { |
e166ed4e BA |
13 | n = nrow(Y) |
14 | m = ncol(Y) | |
15 | p = ncol(X) | |
ae4fa2cb | 16 | |
ef67d338 | 17 | Zinit1 = array(0, dim=c(n,20)) |
e166ed4e BA |
18 | betaInit1 = array(0, dim=c(p,m,k,20)) |
19 | sigmaInit1 = array(0, dim = c(m,m,k,20)) | |
20 | phiInit1 = array(0, dim = c(p,m,k,20)) | |
21 | rhoInit1 = array(0, dim = c(m,m,k,20)) | |
ae4fa2cb | 22 | Gam = matrix(0, n, k) |
e166ed4e BA |
23 | piInit1 = matrix(0,20,k) |
24 | gamInit1 = array(0, dim=c(n,k,20)) | |
25 | LLFinit1 = list() | |
26 | ||
27 | require(MASS) #Moore-Penrose generalized inverse of matrix | |
e166ed4e BA |
28 | for(repet in 1:20) |
29 | { | |
4725af56 BG |
30 | distance_clus = dist(X) |
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) { |
39 | betaInit1[,,r,repet] = ginv(crossprod(t(X[Z_indice,]))) %*% | |
40 | crossprod(t(X[Z_indice,]), Y[Z_indice,]) | |
41 | } else { | |
ef67d338 BA |
42 | betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*% |
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 | ||
f227455a | 65 | #new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], |
66 | # gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4) | |
67 | new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4) | |
e166ed4e BA |
68 | LLFEessai = new_EMG$LLF |
69 | LLFinit1[repet] = LLFEessai[length(LLFEessai)] | |
70 | } | |
71 | ||
72 | b = which.max(LLFinit1) | |
73 | phiInit = phiInit1[,,,b] | |
74 | rhoInit = rhoInit1[,,,b] | |
75 | piInit = piInit1[b,] | |
76 | gamInit = gamInit1[,,b] | |
77 | ||
78 | return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) | |
39046da6 | 79 | } |