+#' 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))
+}