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
-#' @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 n sample size
+#' @param n            sample size
 #'
 #' @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]
-
+       
        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)
-               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]])
        }
-
+       
        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)
 {
-       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)
        {
-               covX[,,r] = diag(p)
                covY[,,r] = diag(m)
        }
        pi = rep(1./k,k)