prepare EMGLLF / EMGrank wrappers, simplify folder generateTestData

[valse.git] / R / gridLambda.R

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 66b6cc2..855b4a6 100644 (file)
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,3 +1,14 @@
+#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
+#' @param phiInit value for phi
+#' @param rhoInt       value for rho
+#' @param piInit       value for pi
+#' @param gamInit value for gamma
+#' @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)
@@ -5,7 +16,7 @@ gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi
        m = dim(phiInit)[2]
        k = dim(phiInit)[3]
 
-       list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+       list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
 
        grid = array(0, dim=c(p,m,k))
        for (i in 1:p)