-library(MASS) #generalized inverse of matrix Monroe-Penrose
-
-vec_bin = function(X,r){
- Z = c()
- indice = c()
- j=1
- for(i in 1:length(X)){
- if(X[i] == r){
- Z[i] = 1
- indice[j] = i
- j=j+1
- }
- else{
- Z[i] = 0
- }
- }
- return(list(Z,indice))
-}
-
-initSmallEM = function(k,X,Y,tau){
+#' initialization of the EM algorithm
+#'
+#' @param k number of components
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param tau threshold to stop EM algorithm
+#'
+#' @return a list with phiInit, rhoInit, piInit, gamInit
+#' @export
+initSmallEM = function(k,X,Y,tau)
+{
n = nrow(Y)
m = ncol(Y)
p = ncol(X)
-
+
betaInit1 = array(0, dim=c(p,m,k,20))
sigmaInit1 = array(0, dim = c(m,m,k,20))
phiInit1 = array(0, dim = c(p,m,k,20))
gamInit1 = array(0, dim=c(n,k,20))
LLFinit1 = list()
-
- for(repet in 1:20){
- clusters = hclust(dist(y)) #default distance : euclidean
- clusterCut = cutree(clusters,k)
- Zinit1[,repet] = clusterCut #retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique (nb de cluster = k)
+ require(MASS) #Moore-Penrose generalized inverse of matrix
+ require(mclust) # K-means with selection of K
+ for(repet in 1:20)
+ {
+ clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+ Zinit1[,repet] = clusters$classification
- for(r in 1:k){
+ for(r in 1:k)
+ {
Z = Zinit1[,repet]
Z_bin = vec_bin(Z,r)
- Z_vec = Z_bin[[1]] #vecteur 0 et 1 aux endroits où Z==r
- Z_indice = Z_bin[[2]] #renvoit les indices où Z==r
+ Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
+ Z_indice = Z_bin$indice #renvoit les indices o? Z==r
- betaInit1[,,r,repet] = ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+ betaInit1[,,r,repet] =
+ ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
sigmaInit1[,,r,repet] = diag(m)
phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
piInit1[repet,r] = sum(Z_vec)/n
}
- for(i in 1:n){
- for(r in 1:k){
- dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
+ for(i in 1:n)
+ {
+ for(r in 1:k)
+ {
+ dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
+ (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
}
sumGamI = sum(gam[i,])
miniInit = 10
maxiInit = 11
- new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
- ##.C("EMGLLF", phiInit = phiInit, rhoInit = rhoInit, ...)
- LLFEessai = new_EMG[[4]]
- LLFinit1[[repet]] = LLFEessai[[length(LLFEessai)]]
+ new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+ gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
+ LLFEessai = new_EMG$LLF
+ LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}
b = which.max(LLFinit1)
-
phiInit = phiInit1[,,,b]
rhoInit = rhoInit1[,,,b]
piInit = piInit1[b,]
gamInit = gamInit1[,,b]
- return(list(phiInit, rhoInit, piInit, gamInit))
+ return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
}
-
-