X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgenerateSampleInputs.R;fp=R%2FgenerateSampleInputs.R;h=fb67b084f1dcd917c1708dbd154e09a19b8f2fc5;hp=0000000000000000000000000000000000000000;hb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90;hpb=c3bc47052f3ccb659659c59a82e9a99ea842398d

diff --git a/R/generateSampleInputs.R b/R/generateSampleInputs.R
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+#' Generate a sample of (X,Y) of size n
+#' @param meanX matrix of group means for covariates (of size p*K)
+#' @param covX covariance for covariates (of size p*p*K)
+#' @param covY covariance for the response vector (of size m*m*K)
+#' @param pi	 proportion for each cluster
+#' @param beta regression matrix
+#' @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(covX)[3]
+
+	X = matrix(nrow=n,ncol=p)
+	Y = matrix(nrow=n,ncol=m)
+
+	require(MASS) #simulate from a multivariate normal distribution
+	for (i in 1:n)
+	{
+		class = sample(1:k, 1, prob=pi)
+		X[i,] = mvrnorm(1, meanX[,class], covX[,,class])
+		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class], covY[,,class])
+	}
+
+	return (list(X=X,Y=Y))
+}
+
+#' 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)
+{
+	rangeX = 100
+	meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
+	covX = array(dim=c(p,p,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)
+	#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))
+}