X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgenerateSampleInputs.R;fp=pkg%2FR%2FgenerateSampleInputs.R;h=0000000000000000000000000000000000000000;hb=086ca318ed5580e961ceda3f1e122a2da58e4427;hp=c7aa3c6a78bd2c0b033467b3fa317d00af0dd5a4;hpb=4e8267487c83c27273305b1379e44bc7abebf4b5;p=valse.git

diff --git a/pkg/R/generateSampleInputs.R b/pkg/R/generateSampleInputs.R
deleted file mode 100644
index c7aa3c6..0000000
--- a/pkg/R/generateSampleInputs.R
+++ /dev/null
@@ -1,86 +0,0 @@
-#' Generate a sample of (X,Y) of size n
-#' @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
-#'
-#' @return list with X and Y
-#' @export
-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, covX)
-		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
-	}
-	
-	return (list(X=X,Y=Y, class = class))
-}
-
-#' Generate a sample of (X,Y) of size n with default values
-#' @param n sample size
-#' @param p number of covariates
-#' @param m size of the response
-#' @param k number of clusters
-#' @return list with X and Y
-#' @export
-generateXYdefault = function(n, p, m, k)
-{
-	meanX = rep(0, p)
-	covX = diag(p)
-	covY = array(dim=c(m,m,k))
-	for(r in 1:k)
-	{
-		covY[,,r] = diag(m)
-	}
-	pi = rep(1./k,k)
-	#initialize beta to a random number of non-zero random value
-	beta = array(0, dim=c(p,m,k))
-	for (j in 1:p)
-	{
-		nonZeroCount = sample(1:m, 1)
-		beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
-	}
-
-	sample_IO = generateXY(meanX, covX, covY, pi, beta, n)
-	return (list(X=sample_IO$X,Y=sample_IO$Y))
-}
-
-#' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
-#' identity for covariance matrices, and uniformly distributed for the clustering)
-#' @param n sample size
-#' @param p number of covariates
-#' @param m size of the response
-#' @param k number of clusters
-#' @return list with phiInit, rhoInit,piInit,gamInit
-#' @export
-basicInitParameters = function(n,p,m,k)
-{
-	phiInit = array(0, dim=c(p,m,k))
-
-	piInit = (1./k)*rep(1,k)
-
-	rhoInit = array(dim=c(m,m,k))
-	for (i in 1:k)
-		rhoInit[,,i] = diag(m)
-
-	gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
-	R = sample(1:k, n, replace=TRUE)
-	for (i in 1:n)
-		gamInit[i,R[i]] = 0.9
-	gamInit = gamInit/sum(gamInit[1,])
-
-	return (list("phiInit" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit))
-}