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re-indent and remove prints in generateSampleInputs
[valse.git]
/
pkg
/
R
/
generateSampleInputs.R
diff --git
a/pkg/R/generateSampleInputs.R
b/pkg/R/generateSampleInputs.R
index
7ec361f
..
c7aa3c6
100644
(file)
--- a/
pkg/R/generateSampleInputs.R
+++ b/
pkg/R/generateSampleInputs.R
@@
-1,10
+1,10
@@
#' Generate a sample of (X,Y) of size n
#' Generate a sample of (X,Y) of size n
-#' @param meanX matrix of group means for covariates (
p x K
)
-#' @param covX covariance for covariates (
p x p x K
)
-#' @param covY covariance for the response vector (
m x m x
K)
-#' @param pi 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 covY covariance for the response vector (
of size m*m*
K)
+#' @param pi
proportion for each cluster
#' @param beta regression matrix, of size p*m*k
#' @param beta regression matrix, of size p*m*k
-#' @param n sample size
+#' @param n
sample size
#'
#' @return list with X and Y
#' @export
#'
#' @return list with X and Y
#' @export
@@
-13,19
+13,19
@@
generateXY = function(meanX, covX, covY, pi, beta, n)
p = dim(covX)[1]
m = dim(covY)[1]
k = dim(covY)[3]
p = dim(covX)[1]
m = dim(covY)[1]
k = dim(covY)[3]
-
+
X = matrix(nrow=n,ncol=p)
Y = matrix(nrow=n,ncol=m)
class = matrix(nrow = n)
X = matrix(nrow=n,ncol=p)
Y = matrix(nrow=n,ncol=m)
class = matrix(nrow = n)
-
+
require(MASS) #simulate from a multivariate normal distribution
for (i in 1:n)
{
class[i] = sample(1:k, 1, prob=pi)
require(MASS) #simulate from a multivariate normal distribution
for (i in 1:n)
{
class[i] = sample(1:k, 1, prob=pi)
- X[i,] = mvrnorm(1, meanX
[,class[i]], covX[,,class[i]]
)
+ X[i,] = mvrnorm(1, meanX
, covX
)
Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
}
Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
}
-
+
return (list(X=X,Y=Y, class = class))
}
return (list(X=X,Y=Y, class = class))
}
@@
-38,13
+38,11
@@
generateXY = function(meanX, covX, covY, pi, beta, n)
#' @export
generateXYdefault = function(n, p, m, k)
{
#' @export
generateXYdefault = function(n, p, m, k)
{
- rangeX = 100
- meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
- covX = array(dim=c(p,p,k))
+ meanX = rep(0, p)
+ covX = diag(p)
covY = array(dim=c(m,m,k))
for(r in 1:k)
{
covY = array(dim=c(m,m,k))
for(r in 1:k)
{
- covX[,,r] = diag(p)
covY[,,r] = diag(m)
}
pi = rep(1./k,k)
covY[,,r] = diag(m)
}
pi = rep(1./k,k)