update Emilie

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

diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
index fe86045..fde4b0f 100644 (file)
--- a/pkg/R/generateXY.R
+++ b/pkg/R/generateXY.R
@@ -1,39 +1,39 @@
-#' generateXY 
+#' generateXY
 #'
 #' Generate a sample of (X,Y) of size n
 #'
 #' @param n sample size
-#' @param π proportion for each cluster
+#' @param prop proportion for each cluster
 #' @param meanX matrix of group means for covariates (of size p)
 #' @param covX covariance for covariates (of size p*p)
-#' @param β regression matrix, of size p*m*k
-#' @param covY covariance for the response vector (of size m*m*K)
+#' @param beta regression matrix, of size p*m*k
+#' @param covY covariance for the response vector (of size m*m)
 #'
 #' @return list with X and Y
 #'
 #' @export
-generateXY <- function(n, π, meanX, β, covX, covY)
+generateXY <- function(n, prop, meanX, beta, covX, covY)
 {
   p <- dim(covX)[1]
   m <- dim(covY)[1]
-  k <- dim(covY)[3]
-  
+  k <- dim(beta)[3]
+
   X <- matrix(nrow = 0, ncol = p)
   Y <- matrix(nrow = 0, ncol = m)
-  
+
   # random generation of the size of each population in X~Y (unordered)
-  sizePop <- rmultinom(1, n, π)
-  class <- c()  #map i in 1:n --> index of class in 1:k
-  
+  sizePop <- stats::rmultinom(1, n, prop)
+  class <- c() #map i in 1:n --> index of class in 1:k
+
   for (i in 1:k)
   {
     class <- c(class, rep(i, sizePop[i]))
     newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
     X <- rbind(X, newBlockX)
-    Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*% 
-      β[, , i], covY[, , i]))))
+    Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*%
+      beta[, , i], covY[,]))))
   }
-  
+
   shuffle <- sample(n)
   list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
 }