| 1 | library(MASS) #generalized inverse of matrix Monroe-Penrose |
| 2 | |
| 3 | vec_bin = function(X,r){ |
| 4 | Z = c() |
| 5 | indice = c() |
| 6 | j=1 |
| 7 | for(i in 1:length(X)){ |
| 8 | if(X[i] == r){ |
| 9 | Z[i] = 1 |
| 10 | indice[j] = i |
| 11 | j=j+1 |
| 12 | } |
| 13 | else{ |
| 14 | Z[i] = 0 |
| 15 | } |
| 16 | } |
| 17 | return(list(Z,indice)) |
| 18 | } |
| 19 | |
| 20 | initSmallEM = function(k,X,Y,tau){ |
| 21 | n = nrow(Y) |
| 22 | m = ncol(Y) |
| 23 | p = ncol(X) |
| 24 | |
| 25 | betaInit1 = array(0, dim=c(p,m,k,20)) |
| 26 | sigmaInit1 = array(0, dim = c(m,m,k,20)) |
| 27 | phiInit1 = array(0, dim = c(p,m,k,20)) |
| 28 | rhoInit1 = array(0, dim = c(m,m,k,20)) |
| 29 | piInit1 = matrix(0,20,k) |
| 30 | gamInit1 = array(0, dim=c(n,k,20)) |
| 31 | LLFinit1 = list() |
| 32 | |
| 33 | |
| 34 | for(repet in 1:20){ |
| 35 | clusters = hclust(dist(y)) #default distance : euclidean |
| 36 | clusterCut = cutree(clusters,k) |
| 37 |