p <- dim(covX)[1]
m <- dim(covY)[1]
k <- dim(covY)[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, pi)
+ sizePop <- rmultinom(1, n, π)
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, apply( newBlockX, 1, function(row)
- mvrnorm(1, row %*% beta[,,i], covY[,,i]) ) )
+ Y <- rbind( Y, t(apply( newBlockX, 1, function(row)
+ MASS::mvrnorm(1, row %*% β[,,i], covY[,,i]) )) )
}
shuffle = sample(n)