no need to generate random IO params: migrate in test. Add roxygen2 NAMESPACE-generat...

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

diff --git a/pkg/R/gridLambda.R b/pkg/R/computeGridLambda.R
similarity index 63%
rename from pkg/R/gridLambda.R
rename to pkg/R/computeGridLambda.R
index 35c412a..f89b2a3 100644 (file)
--- a/pkg/R/gridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,4 +1,7 @@
+#' computeGridLambda
+#'
 #' 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
@@ -6,20 +9,20 @@
 #' @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
+#' @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)
+computeGridLambda = 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)
@@ -29,6 +32,5 @@ gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi
        }
        grid = unique(grid)
        grid = grid[grid <=1]
-
-       return(grid)
+       grid
 }