Folder reorganization

[valse.git] / pkg / R / gridLambda.R

diff --git a/pkg/R/gridLambda.R b/pkg/R/gridLambda.R
new file mode 100644 (file)
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+++ b/pkg/R/gridLambda.R
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+#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
+#' @param phiInit value for phi
+#' @param rhoInit      value for rho
+#' @param piInit       value for pi
+#' @param gamInit value for gamma
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param gamma power of weights in the penalty
+#' @param mini         minimum number of iterations in EM algorithm
+#' @param maxi         maximum number of iterations in EM algorithm
+#' @param tau          threshold to stop EM algorithm
+#' @return the grid of regularization parameters
+#' @export
+#-----------------------------------------------------------------------
+gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
+{
+       n = nrow(X)
+       p = dim(phiInit)[1]
+       m = dim(phiInit)[2]
+       k = dim(phiInit)[3]
+
+       #list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+  list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+       grid = array(0, dim=c(p,m,k))
+       for (i in 1:p)
+       {
+               for (j in 1:m)
+                       grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
+       }
+       grid = unique(grid)
+       grid = grid[grid <=1]
+
+       return(grid)
+}