test/generate_test_data/helper.R

   1 #' Generate a sample of (X,Y) of size n with default values
   2 #' @param n sample size
   3 #' @param p number of covariates
   4 #' @param m size of the response
   5 #' @param k number of clusters
   6 #' @return list with X and Y
   7 #' @export
   8 generateXYdefault = function(n, p, m, k)
   9 {
  10     meanX = rep(0, p)
  11     covX = diag(p)
  12     covY = array(dim=c(m,m,k))
  13     for(r in 1:k)
  14         covY[,,r] = diag(m)
  15     pi = rep(1./k,k)
  16     #initialize beta to a random number of non-zero random value
  17     beta = array(0, dim=c(p,m,k))
  18     for (j in 1:p)
  19     {
  20         nonZeroCount = sample(1:m, 1)
  21         beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
  22     }
  23
  24     sample_IO = generateXY(meanX, covX, covY, pi, beta, n)
  25     return (list(X=sample_IO$X,Y=sample_IO$Y))
  26 }
  27
  28 #' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
  29 #' identity for covariance matrices, and uniformly distributed for the clustering)
  30 #' @param n sample size
  31 #' @param p number of covariates
  32 #' @param m size of the response
  33 #' @param k number of clusters
  34 #' @return list with phiInit, rhoInit,piInit,gamInit
  35 #' @export
  36 basicInitParameters = function(n,p,m,k)
  37 {
  38     phiInit = array(0, dim=c(p,m,k))
  39
  40     piInit = (1./k)*rep(1,k)
  41
  42     rhoInit = array(dim=c(m,m,k))
  43     for (i in 1:k)
  44         rhoInit[,,i] = diag(m)
  45
  46     gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
  47     R = sample(1:k, n, replace=TRUE)
  48     for (i in 1:n)
  49         gamInit[i,R[i]] = 0.9
  50     gamInit = gamInit/sum(gamInit[1,])
  51
  52     return (list("phiInit" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit))
  53 }