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Adjustments for CRAN upload
[valse.git]
/
pkg
/
R
/
generateXY.R
diff --git
a/pkg/R/generateXY.R
b/pkg/R/generateXY.R
index
064b54b
..
6b811d6
100644
(file)
--- 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
#'
#' 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 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
#'
#' @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]
{
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)
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)
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)
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)
}
shuffle <- sample(n)