fix generateRunTest for constructionModelesEMGrank; remove man pages (will be roxygen...

[valse.git] / R / generateSampleInputs.R

diff --git a/R/generateSampleInputs.R b/R/generateSampleInputs.R
new file mode 100644 (file)
index 0000000..fb67b08
--- /dev/null
+++ b/R/generateSampleInputs.R
@@ -0,0 +1,87 @@
+#' 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))
+}