X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2Fvalse.R;fp=R%2Fvalse.R;h=e5205a5e61a28cae4d237756471f8cb020f1dee2;hp=0000000000000000000000000000000000000000;hb=c7dab9ff8b95a7630c7dafdcf40d60c659290ef2;hpb=e3f2fe8a918614d246fe2451065b0dfcd348b366

diff --git a/R/valse.R b/R/valse.R
new file mode 100644
index 0000000..e5205a5
--- /dev/null
+++ b/R/valse.R
@@ -0,0 +1,121 @@
+#' Main function
+#'
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param procedure among 'LassoMLE' or 'LassoRank'
+#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'
+#' @param gamma integer for the power in the penaly, by default = 1
+#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
+#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
+#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
+#' @param kmin integer, minimum number of clusters, by default = 2
+#' @param kmax integer, maximum number of clusters, by default = 10
+#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
+#' @param rang.max integer, maximum rank in the
+#' @return a list with estimators of parameters
+#' @export
+#-----------------------------------------------------------------------
+valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
+                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+                 rang.min = 1,rang.max = 10) {
+  ##################################
+  #core workflow: compute all models
+  ##################################
+  
+  p = dim(phiInit)[1]
+  m = dim(phiInit)[2]
+  
+  print("main loop: over all k and all lambda")
+  for (k in kmin:kmax)
+  {
+    print(k)
+    
+    print("Parameters initialization")
+    #smallEM initializes parameters by k-means and regression model in each component,
+    #doing this 20 times, and keeping the values maximizing the likelihood after 10
+    #iterations of the EM algorithm.
+    init = initSmallEM(k, X, Y)
+    phiInit <<- init$phiInit
+    rhoInit <<- init$rhoInit
+    piInit	<<- init$piInit
+    gamInit <<- init$gamInit
+    
+    gridLambda <<- gridLambda(phiInit, rhoInit, piInit, tauInit, X, Y, gamma, mini, maxi, eps)
+    
+    print("Compute relevant parameters")
+    #select variables according to each regularization parameter
+    #from the grid: A1 corresponding to selected variables, and
+    #A2 corresponding to unselected variables.
+    params = selectiontotale(phiInit,rhoInit,piInit,tauInit,
+                             mini,maxi,gamma,gridLambda,
+                             X,Y,thresh,eps)
+    A1 <<- params$A1
+    A2 <<- params$A2
+    Rho <<- params$Rho
+    Pi <<- params$Pi
+    
+    if (procedure == 'LassoMLE') {
+      print('run the procedure Lasso-MLE')
+      #compute parameter estimations, with the Maximum Likelihood
+      #Estimator, restricted on selected variables.
+      model = constructionModelesLassoMLE(
+        phiInit, rhoInit,piInit,tauInit,mini,maxi,
+        gamma,gridLambda,X,Y,thresh,eps,A1,A2)
+      ################################################
+      ### Regarder la SUITE
+      r1 = runProcedure1()
+      Phi2 = Phi
+      Rho2 = Rho
+      Pi2 = Pi
+      
+      if (is.null(dim(Phi2)))
+        #test was: size(Phi2) == 0
+      {
+        Phi[, , 1:k] <<- r1$phi
+        Rho[, , 1:k] <<- r1$rho
+        Pi[1:k,] <<- r1$pi
+      } else
+      {
+        Phi <<-
+          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
+        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
+        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
+        Rho <<-
+          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
+        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
+        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
+        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
+        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
+        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
+      }
+    } else {
+      print('run the procedure Lasso-Rank')
+      #compute parameter estimations, with the Low Rank
+      #Estimator, restricted on selected variables.
+      model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
+                                           A1, rank.min, rank.max)
+      
+      ################################################
+      ### Regarder la SUITE  
+      phi = runProcedure2()$phi
+      Phi2 = Phi
+      if (dim(Phi2)[1] == 0)
+      {
+        Phi[, , 1:k,] <<- phi
+      } else
+      {
+        Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
+        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
+        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
+      }
+    }
+  }
+  print('Model selection')
+  if (selecMod == 'SlopeHeuristic') {
+    
+  } else if (selecMod == 'BIC') {
+    
+  } else if (selecMod == 'AIC') {
+    
+  }
+}