+#' initSmallEM
+#'
#' 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 fast boolean to enable or not the C function call
+#'
+#' @return a list with phiInit (the regression parameter reparametrized),
+#' rhoInit (the covariance parameter reparametrized), piInit (the proportion parameter is the
+#' mixture model), gamInit (the conditional expectation)
#'
-#' @return a list with phiInit, rhoInit, piInit, gamInit
-#' @export
-#' @importFrom methods new
#' @importFrom stats cutree dist hclust runif
+#'
+#' @export
initSmallEM <- function(k, X, Y, fast)
{
n <- nrow(X)
for (r in 1:k)
{
Z <- Zinit1[, repet]
- Z_indice <- seq_len(n)[Z == r] #renvoit les indices oรน Z==r
+ Z_indice <- seq_len(n)[Z == r] #renvoit les indices ou Z==r
if (length(Z_indice) == 1) {
betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*%
crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
piInit <- piInit1[b, ]
gamInit <- gamInit1[, , b]
- return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
+ list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)
}