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 = diag(m)
  15     ω = rep(1./k,k)
  16     #initialize beta to a random number of non-zero random value
  17     β = array(0, dim=c(p,m,k))
  18     for (j in 1:p)
  19     {
  20         nonZeroCount = sample(1:m, 1)
  21         if (nonZeroCount >= 2)
  22             β[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
  23         else
  24             β[j,1,] = runif(k)
  25     }
  26
  27     sample_IO = generateXY(n, ω, meanX, β, covX, covY)
  28     return (list(X=sample_IO$X,Y=sample_IO$Y))
  29 }
  30
  31 #' Initialize the parameters in a basic way (zero for the conditional mean, uniform for
  32 #' weights, identity for covariance matrices, and uniformly distributed for the
  33 #' clustering)
  34 #'
  35 #' @param n sample size
  36 #' @param p number of covariates
  37 #' @param m size of the response
  38 #' @param k number of clusters
  39 #'
  40 #' @return list with phiInit, rhoInit,piInit,gamInit
  41 #'
  42 basicInitParameters = function(n,p,m,k)
  43 {
  44     phiInit = array(0, dim=c(p,m,k))
  45
  46     piInit = (1./k)*rep(1,k)
  47
  48     rhoInit = array(dim=c(m,m,k))
  49     for (i in 1:k)
  50         rhoInit[,,i] = diag(m)
  51
  52     gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
  53     R = sample(1:k, n, replace=TRUE)
  54     for (i in 1:n)
  55         gamInit[i,R[i]] = 0.9
  56     gamInit = gamInit/sum(gamInit[1,])
  57
  58     return (list("phiInit"=phiInit, "rhoInit"=rhoInit, "piInit"=piInit, "gamInit"=gamInit))
  59 }