test/helper.R

   1 #' Generate a sample of (X,Y) of size n with default values
   2 #'
   3 #' @param n sample size
   4 #' @param p number of covariates
   5 #' @param m size of the response
   6 #' @param k number of clusters
   7 #'
   8 #' @return list with X and Y
   9 #'
  10 generateXYdefault = function(n, p, m, k)
  11 {
  12     meanX = rep(0, p)
  13     covX = diag(p)
  14     covY = array(dim=c(m,m,k))
  15     for(r in 1:k)
  16         covY[,,r] = diag(m)
  17     π = rep(1./k,k)
  18     #initialize beta to a random number of non-zero random value
  19     β = array(0, dim=c(p,m,k))
  20     for (j in 1:p)
  21     {
  22         nonZeroCount = sample(1:m, 1)
  23         β[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
  24     }
  25
  26     sample_IO = generateXY(n, π, meanX, β, covX, covY)
  27     return (list(X=sample_IO$X,Y=sample_IO$Y))
  28 }
  29
  30 #' Initialize the parameters in a basic way (zero for the conditional mean, uniform for
  31 #' weights, identity for covariance matrices, and uniformly distributed for the
  32 #' clustering)
  33 #'
  34 #' @param n sample size
  35 #' @param p number of covariates
  36 #' @param m size of the response
  37 #' @param k number of clusters
  38 #'
  39 #' @return list with phiInit, rhoInit,piInit,gamInit
  40 #'
  41 basicInitParameters = function(n,p,m,k)
  42 {
  43     phiInit = array(0, dim=c(p,m,k))
  44
  45     piInit = (1./k)*rep(1,k)
  46
  47     rhoInit = array(dim=c(m,m,k))
  48     for (i in 1:k)
  49         rhoInit[,,i] = diag(m)
  50
  51     gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
  52     R = sample(1:k, n, replace=TRUE)
  53     for (i in 1:n)
  54         gamInit[i,R[i]] = 0.9
  55     gamInit = gamInit/sum(gamInit[1,])
  56
  57     return (list("phiInit"=phiInit, "rhoInit"=rhoInit, "piInit"=piInit, "gamInit"=gamInit))
  58 }