#' 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)
{
meanX = rep(0, p)
covY = array(dim=c(m,m,k))
for(r in 1:k)
covY[,,r] = diag(m)
- pi = rep(1./k,k)
+ π = rep(1./k,k)
#initialize beta to a random number of non-zero random value
- beta = array(0, dim=c(p,m,k))
+ β = 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)
+ β[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
}
- sample_IO = generateXY(meanX, covX, covY, pi, beta, n)
+ sample_IO = generateXY(n, π, meanX, β, covX, covY)
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)
+#' 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))
gamInit[i,R[i]] = 0.9
gamInit = gamInit/sum(gamInit[1,])
- return (list("phiInit" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit))
+ return (list("phiInit"=phiInit, "rhoInit"=rhoInit, "piInit"=piInit, "gamInit"=gamInit))
}