#' @export
#' @importFrom methods new
#' @importFrom stats cutree dist hclust runif
-initSmallEM = function(k,X,Y)
+initSmallEM = function(k,X,Y, fast=TRUE)
{
n = nrow(Y)
m = ncol(Y)
gamInit1 = array(0, dim=c(n,k,20))
LLFinit1 = list()
- require(MASS) #Moore-Penrose generalized inverse of matrix
+ #require(MASS) #Moore-Penrose generalized inverse of matrix
for(repet in 1:20)
{
distance_clus = dist(X)
Z = Zinit1[,repet]
Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
if (length(Z_indice) == 1) {
- betaInit1[,,r,repet] = ginv(crossprod(t(X[Z_indice,]))) %*%
+ betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
crossprod(t(X[Z_indice,]), Y[Z_indice,])
} else {
- betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+ betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
crossprod(X[Z_indice,], Y[Z_indice,])
}
sigmaInit1[,,r,repet] = diag(m)
miniInit = 10
maxiInit = 11
- #new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-# gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
- new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
+ new_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+ gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
LLFEessai = new_EMG$LLF
LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}