#' @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)
+#' @importFrom methods new
+#' @importFrom stats cutree dist hclust runif
+initSmallEM = function(k,X,Y)
{
n = nrow(Y)
m = ncol(Y)
p = ncol(X)
- Zinit1 = array(0, dim=c(n,20))
+ Zinit1 = array(0, dim=c(n,20))
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))
for(r in 1:k)
{
Z = Zinit1[,repet]
- Z_bin = vec_bin(Z,r)
- Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
- Z_indice = Z_bin$indice #renvoit les indices o? Z==r
-
- betaInit1[,,r,repet] = ginv( crossprod(X[Z_indice,]) ) %*% crossprod(X[Z_indice,], Y[Z_indice,])
+ 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,]))) %*%
+ crossprod(t(X[Z_indice,]), Y[Z_indice,])
+ } else {
+ betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+ crossprod(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
+ piInit1[repet,r] = mean(Z == r)
}
for(i in 1:n)
miniInit = 10
maxiInit = 11
- new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
+ #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)
LLFEessai = new_EMG$LLF
LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}