pkg/R/generateSampleInputs.R

   1 #' Generate a sample of (X,Y) of size n
   2 #' @param meanX matrix of group means for covariates (p x K)
   3 #' @param covX covariance for covariates (p x p x K)
   4 #' @param covY covariance for the response vector (m x m x K)
   5 #' @param pi proportion for each cluster
   6 #' @param beta regression matrix, of size p*m*k
   7 #' @param n sample size
   8 #'
   9 #' @return list with X and Y
  10 #' @export
  11 generateXY = function(meanX, covX, covY, pi, beta, n)
  12 {
  13     p = dim(covX)[1]
  14     m = dim(covY)[1]
  15     k = dim(covY)[3]
  16
  17     X = matrix(nrow=n,ncol=p)
  18     Y = matrix(nrow=n,ncol=m)
  19     class = matrix(nrow = n)
  20
  21     require(MASS) #simulate from a multivariate normal distribution
  22     for (i in 1:n)
  23     {
  24         class[i] = sample(1:k, 1, prob=pi)
  25         X[i,] = mvrnorm(1, meanX[,class[i]], covX[,,class[i]])
  26         Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
  27     }
  28
  29     return (list(X=X,Y=Y, class = class))
  30 }
  31
  32 #' Generate a sample of (X,Y) of size n with default values
  33 #' @param n sample size
  34 #' @param p number of covariates
  35 #' @param m size of the response
  36 #' @param k number of clusters
  37 #' @return list with X and Y
  38 #' @export
  39 generateXYdefault = function(n, p, m, k)
  40 {
  41     rangeX = 100
  42     meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
  43     covX = array(dim=c(p,p,k))
  44     covY = array(dim=c(m,m,k))
  45     for(r in 1:k)
  46     {
  47         covX[,,r] = diag(p)
  48         covY[,,r] = diag(m)
  49     }
  50     pi = rep(1./k,k)
  51     #initialize beta to a random number of non-zero random value
  52     beta = array(0, dim=c(p,m,k))
  53     for (j in 1:p)
  54     {
  55         nonZeroCount = sample(1:m, 1)
  56         beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
  57     }
  58
  59     sample_IO = generateXY(meanX, covX, covY, pi, beta, n)
  60     return (list(X=sample_IO$X,Y=sample_IO$Y))
  61 }
  62
  63 #' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
  64 #' identity for covariance matrices, and uniformly distributed for the clustering)
  65 #' @param n sample size
  66 #' @param p number of covariates
  67 #' @param m size of the response
  68 #' @param k number of clusters
  69 #' @return list with phiInit, rhoInit,piInit,gamInit
  70 #' @export
  71 basicInitParameters = function(n,p,m,k)
  72 {
  73     phiInit = array(0, dim=c(p,m,k))
  74
  75     piInit = (1./k)*rep(1,k)
  76
  77     rhoInit = array(dim=c(m,m,k))
  78     for (i in 1:k)
  79         rhoInit[,,i] = diag(m)
  80
  81     gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
  82     R = sample(1:k, n, replace=TRUE)
  83     for (i in 1:n)
  84         gamInit[i,R[i]] = 0.9
  85     gamInit = gamInit/sum(gamInit[1,])
  86
  87     return (list("phiInit" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit))
  88 }