X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FgenerateXY.R;h=6b811d6bbb0173c6115697331a9e1028a5ee1b96;hp=064b54b2d083bb3bc9d9ddd26a8654962e5b93c2;hb=6af1d4897dbab92a7be05068e0e15823378965d9;hpb=1b698c1619dbcf5b3a0608dc894d249945d2bce3

diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
index 064b54b..6b811d6 100644
--- a/pkg/R/generateXY.R
+++ b/pkg/R/generateXY.R
@@ -1,28 +1,28 @@
-#' 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
+#' @return list with X (of size n*p) and Y (of size n*m)
 #'
 #' @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)
+  sizePop <- stats::rmultinom(1, n, prop)
   class <- c() #map i in 1:n --> index of class in 1:k
 
   for (i in 1:k)
@@ -30,8 +30,8 @@ generateXY <- function(n, π, meanX, β, covX, covY)
     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)