Adjustments for CRAN upload

[valse.git] / pkg / R / initSmallEM.R

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index fafa2c4..3945322 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -1,18 +1,24 @@
-#' initialization of the EM algorithm 
+#' 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
-initSmallEM <- function(k, X, Y, fast = TRUE)
+#'
+#' @export
+initSmallEM <- function(k, X, Y, fast)
 {
-  n <- nrow(Y)
-  m <- ncol(Y)
+  n <- nrow(X)
   p <- ncol(X)
+  m <- ncol(Y)
   nIte <- 20
   Zinit1 <- array(0, dim = c(n, nIte))
   betaInit1 <- array(0, dim = c(p, m, k, nIte))
@@ -23,25 +29,23 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
   piInit1 <- matrix(0, nIte, k)
   gamInit1 <- array(0, dim = c(n, k, nIte))
   LLFinit1 <- list()
-  
+
   # require(MASS) #Moore-Penrose generalized inverse of matrix
   for (repet in 1:nIte)
   {
     distance_clus <- dist(cbind(X, Y))
     tree_hier <- hclust(distance_clus)
     Zinit1[, repet] <- cutree(tree_hier, k)
-    
+
     for (r in 1:k)
     {
       Z <- Zinit1[, repet]
-      Z_indice <- seq_len(n)[Z == r]  #renvoit les indices où Z==r
-      if (length(Z_indice) == 1)
-      {
-        betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*% 
+      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, ])
-      } else
-      {
-        betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*% 
+      } else {
+        betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*%
           crossprod(X[Z_indice, ], Y[Z_indice, ])
       }
       sigmaInit1[, , r, repet] <- diag(m)
@@ -49,34 +53,34 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
       rhoInit1[, , r, repet] <- solve(sigmaInit1[, , r, repet])
       piInit1[repet, r] <- mean(Z == r)
     }
-    
+
     for (i in 1:n)
     {
       for (r in 1:k)
       {
-        dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet] - X[i, 
-          ] %*% phiInit1[, , r, repet])
-        Gam[i, r] <- piInit1[repet, r] * det(rhoInit1[, , r, repet]) * exp(-0.5 * 
-          dotProduct)
+        dotProduct <- tcrossprod(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, ])
+      # TODO: next line is a division by zero if dotProduct is big
       gamInit1[i, , repet] <- Gam[i, ]/sumGamI
     }
-    
+
     miniInit <- 10
     maxiInit <- 11
-    
-    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, 
-      ], gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, 
-      Y, eps = 1e-04, fast)
-    LLFEessai <- init_EMG$LLF
-    LLFinit1[repet] <- LLFEessai[length(LLFEessai)]
+
+    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, ],
+      gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, Y,
+      eps = 1e-04, fast)
+    LLFinit1[[repet]] <- init_EMG$llh
   }
   b <- which.min(LLFinit1)
   phiInit <- phiInit1[, , , b]
   rhoInit <- rhoInit1[, , , b]
   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)
 }