Merge branch 'master' of auder.net:valse

[valse.git] / R / generateSampleInputs.R

diff --git a/R/generateSampleInputs.R b/R/generateSampleInputs.R
deleted file mode 100644 (file)
index 8edd031..0000000
--- a/R/generateSampleInputs.R
+++ /dev/null
@@ -1,90 +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)
-               print(X[i,])
-               print(beta[,,class[i]])
-               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)
-{
-       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))
-}