ajout des commentaires Roxygen - anglicisme de certains noms de fonctions et de variables

diff --git a/R/basicInitParameters.R b/R/basicInitParameters.R
index 6090d0a..9801229 100644 (file)
--- a/R/basicInitParameters.R
+++ b/R/basicInitParameters.R
@@ -1,18 +1,28 @@
+#-----------------------------------------------------------------------
+#' Initialize the parameters in a basic way (zero for the conditional mean,
+#'  uniform for weights, identity for covariance matrices, and uniformly distributed forthe 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
+#-----------------------------------------------------------------------

 basic_Init_Parameters = function(n,p,m,k)
 {
 basic_Init_Parameters = function(n,p,m,k)
 {

-       phiInit = array(0, dim=c(p,m,k))
-
-       piInit = (1./k)*rep.int(1,k)
-
-       rhoInit = array(0, dim=c(m,m,k))
-       for(i in 1:k)
-               rhoInit[,,i] = diag(m)
-
-       gamInit = 0.1*array(1, dim=c(n,k))
-       R = sample(1:k,n, replace=TRUE)
-       for(i in 1:n)
-               gamInit[i,R[i]] = 0.9
-       gamInit = gamInit/sum(gamInit[1,])
-
-       return (list(phiInit, rhoInit, piInit, gamInit))
+  phiInit = array(0, dim=c(p,m,k))
+  
+  piInit = (1./k)*rep.int(1,k)
+  
+  rhoInit = array(0, dim=c(m,m,k))
+  for(i in 1:k)
+    rhoInit[,,i] = diag(m)
+  
+  gamInit = 0.1*array(1, dim=c(n,k))
+  R = sample(1:k,n, replace=TRUE)
+  for(i in 1:n)
+    gamInit[i,R[i]] = 0.9
+  gamInit = gamInit/sum(gamInit[1,])
+  
+  return (data = list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))

 }
 }

diff --git a/R/discardSimilarModels.R b/R/discardSimilarModels.R
new file mode 100644 (file)
index 0000000..8b12077
--- /dev/null
+++ b/R/discardSimilarModels.R
@@ -0,0 +1,31 @@
+#' Discard models which have the same relevant variables
+#'
+#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
+#' @param B2 array of irrelevant coefficients (of size p*m*length(gridlambda))
+#' @param glambda grid of regularization parameters (vector)
+#' @param rho covariance matrix (of size m*m*K*size(gridLambda))
+#' @param pi weight parameters (of size K*size(gridLambda))
+#'
+#' @return a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
+#'  of selected models.
+#' @export
+discardSimilarModels = function(B1,B2,glambda,rho,pi)
+{
+  ind = c()
+  for (j in 1:length(glambda))
+  {
+    for (ll in 1:(l-1))
+    {
+      if(B1[,,l] == B1[,,ll])
+        ind = c(ind, l)
+    }
+  }
+  ind = unique(ind)
+  B1 = B1[,,-ind]
+  glambda = glambda[-ind]
+  B2 = B2[,,-ind]
+  rho = rho[,,,-ind] 
+  pi = pi[,-ind]
+  
+  return (list(B1=B1,B2=B2,glambda=glambda,rho=rho,pi=pi,ind=ind))
+}

diff --git a/R/discardSimilarModels2.R b/R/discardSimilarModels2.R
new file mode 100644 (file)
index 0000000..d620bff
--- /dev/null
+++ b/R/discardSimilarModels2.R
@@ -0,0 +1,21 @@
+#' Similar to discardSimilarModels, for Lasso-rank procedure (focus on columns)
+#'
+#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
+#' @param rho covariance matrix
+#' @param pi weight parameters
+#'
+#' @return
+#' @export
+#'
+#' @examples
+discardSimilarModels2 = function(B1,rho,pi)
+{      ind = c()
+       dim_B1 = dim(B1)
+       B2 = array(0,dim=c(dim_B1[1],dim_B1[2],dim_B1[3]))
+       sizeLambda=dim_B1[3]
+       glambda = rep(0,sizeLambda)
+
+       suppressmodel = discardSimilarModels(B1,B2,glambda,rho,pi)
+       return (list(B1 = suppressmodel$B1, ind = suppressmodel$B2,
+               rho = suppressmodel$rho, pi = suppressmodel$pi))
+}

diff --git a/R/generateIO.R b/R/generateIO.R
index f8c8194..83d8cc9 100644 (file)
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,26 +1,35 @@
+#' Generate a sample of (X,Y) of size n
+#' @param covX covariance for covariates
+#' @param covY covariance for the response vector
+#' @param pi   proportion for each cluster
+#' @param beta regression matrix
+#' @param n    sample size
+#' @return list with X and Y
+#' @export
+#-----------------------------------------------------------------------

 generateIO = function(covX, covY, pi, beta, n)
 {
 generateIO = function(covX, covY, pi, beta, n)
 {

-       size_covX = dim(covX)
-       p = size_covX[1]
-       k = size_covX[3]
-
-       size_covY = dim(covY)
-       m = size_covY[1]
-
-       Y = matrix(0,n,m)
-       BX = array(0, dim=c(n,m,k))
-
-       require(MASS) #simulate from a multivariate normal distribution
-       for (i in 1:n)
-       {
-               for (r in 1:k)
-               {
-                       BXir = rep(0,m)
-                       for (mm in 1:m)
-                               Bxir[[mm]] = X[i,] %*% beta[,mm,r]
-                       Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
-               }
-       }
-
-       return (list(X=X,Y=Y))
+  size_covX = dim(covX)
+  p = size_covX[1]
+  k = size_covX[3]
+  
+  size_covY = dim(covY)
+  m = size_covY[1]
+  
+  Y = matrix(0,n,m)
+  BX = array(0, dim=c(n,m,k))
+  
+  require(MASS) #simulate from a multivariate normal distribution
+  for (i in 1:n)
+  {
+    for (r in 1:k)
+    {
+      BXir = rep(0,m)
+      for (mm in 1:m)
+        Bxir[[mm]] = X[i,] %*% beta[,mm,r]
+      Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
+    }
+  }
+  
+  return (list(X=X,Y=Y))

 }
 }

diff --git a/R/generateIOdefault.R b/R/generateIOdefault.R
index fc01cd8..85213cc 100644 (file)
--- a/R/generateIOdefault.R
+++ b/R/generateIOdefault.R
@@ -1,22 +1,30 @@
+#' 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
+#-----------------------------------------------------------------------

 generateIOdefault = function(n, p, m, k)
 {
 generateIOdefault = function(n, p, m, k)
 {

-       covX = array(0, dim=c(p,p,k))
-       covY = array(0, dim=c(m,m,k))
-       for(r in 1:k)
-       {
-               covX[,,r] = diag(p)
-               covY[,,r] = diag(m)
-       }
-
-       pi = rep(1./k,k)
-
-       beta = array(0, dim=c(p,m,k))
-       for(j in 1:p)
-       {
-               nonZeroCount = ceiling(m * runif(1))
-               beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
-       }
-
-       sample_IO = generateIO(covX, covY, pi, beta, n)
-       return (list(X=sample_IO$X,Y=sample_IO$Y))
+  covX = array(0, dim=c(p,p,k))
+  covY = array(0, dim=c(m,m,k))
+  for(r in 1:k)
+  {
+    covX[,,r] = diag(p)
+    covY[,,r] = diag(m)
+  }
+  
+  pi = rep(1./k,k)
+  
+  beta = array(0, dim=c(p,m,k))
+  for(j in 1:p)
+  {
+    nonZeroCount = ceiling(m * runif(1))
+    beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
+  }
+  
+  sample_IO = generateIO(covX, covY, pi, beta, n)
+  return (list(X=sample_IO$X,Y=sample_IO$Y))

 }
 }

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 66b6cc2..7b82f63 100644 (file)
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,20 +1,31 @@
+#' 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)
 {
 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",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)
+  n = nrow(X)
+  p = dim(phiInit)[1]
+  m = dim(phiInit)[2]
+  k = dim(phiInit)[3]
+  
+  list_EMG = .Call("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)

 }
 }

diff --git a/R/indicesSelection.R b/R/indicesSelection.R
new file mode 100644 (file)
index 0000000..0445406
--- /dev/null
+++ b/R/indicesSelection.R
@@ -0,0 +1,36 @@
+#' Construct the set of relevant indices
+#'
+#' @param phi regression matrix, of size p*m
+#' @param thresh threshold to say a cofficient is equal to zero
+#'
+#' @return a list with A, a matrix with relevant indices (size = p*m) and B, a 
+#'          matrix with irrelevant indices (size = p*m)
+#' @export
+indicesSelection = function(phi, thresh = 1e-6)
+{
+  dim_phi = dim(phi)
+  p = dim_phi[1]
+  m = dim_phi[2]
+  
+  A = matrix(0, p, m)
+  B = matrix(0, p, m)
+  
+  for(j in 1:p)
+  {
+    cpt1 = 0
+    cpt2 = 0
+    for(mm in 1:m)
+    {
+      if(max(phi[j,mm,]) > thresh)
+      {
+        cpt1 = cpt1 + 1
+        A[j,cpt] = mm
+      } else
+      {
+        cpt2 = cpt2+1
+        B[j, cpt2] = mm
+      }
+    }
+  }
+  return (list(A=A,B=B))
+}

diff --git a/R/initSmallEM.R b/R/initSmallEM.R
index d519766..1fa2d9b 100644 (file)
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -1,84 +1,75 @@
-vec_bin = function(X,r)
-{
-       Z = c()
-       indice = c()
-       j = 1
-       for (i in 1:length(X))
-       {
-               if(X[i] == r)
-               {
-                       Z[i] = 1
-                       indice[j] = i
-                       j=j+1
-               } else
-                       Z[i] = 0
-       }
-       return (list(Z=Z,indice=indice))
-}
-
+#' initialization of the EM algorithm
+#'
+#' @param k number of components
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param tau threshold to stop EM algorithm
+#'
+#' @return a list with phiInit, rhoInit, piInit, gamInit
+#' @export

 initSmallEM = function(k,X,Y,tau)
 {
 initSmallEM = function(k,X,Y,tau)
 {

-       n = nrow(Y)
-       m = ncol(Y)
-       p = ncol(X)
-
-       betaInit1 = array(0, dim=c(p,m,k,20))
-       sigmaInit1 = array(0, dim = c(m,m,k,20))
-       phiInit1 = array(0, dim = c(p,m,k,20))
-       rhoInit1 = array(0, dim = c(m,m,k,20))
-       piInit1 = matrix(0,20,k)
-       gamInit1 = array(0, dim=c(n,k,20))
-       LLFinit1 = list()
-
-       require(MASS) #Moore-Penrose generalized inverse of matrix
-       for(repet in 1:20)
-       {
-               clusters = hclust(dist(y)) #default distance : euclidean
-               #cutree retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique
-               clusterCut = cutree(clusters,k)
-               Zinit1[,repet] = clusterCut
-
-               for(r in 1:k)
-               {
-                       Z = Zinit1[,repet]
-                       Z_bin = vec_bin(Z,r)
-                       Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits où Z==r
-                       Z_indice = Z_bin$indice #renvoit les indices où Z==r
-
-                       betaInit1[,,r,repet] =
-                               ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
-                       sigmaInit1[,,r,repet] = diag(m)
-                       phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
-                       rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
-                       piInit1[repet,r] = sum(Z_vec)/n
-               }
-
-               for(i in 1:n)
-               {
-                       for(r in 1:k)
-                       {
-                               dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
-                                       (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
-                               Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
-                       }
-                       sumGamI = sum(gam[i,])
-                       gamInit1[i,,repet]= Gam[i,] / sumGamI
-               }
-
-               miniInit = 10
-               maxiInit = 11
-
-               new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-                       gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
-               LLFEessai = new_EMG$LLF
-               LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-       }
-
-       b = which.max(LLFinit1)
-       phiInit = phiInit1[,,,b]
-       rhoInit = rhoInit1[,,,b]
-       piInit = piInit1[b,]
-       gamInit = gamInit1[,,b]
-
-       return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+  n = nrow(Y)
+  m = ncol(Y)
+  p = ncol(X)
+  
+  betaInit1 = array(0, dim=c(p,m,k,20))
+  sigmaInit1 = array(0, dim = c(m,m,k,20))
+  phiInit1 = array(0, dim = c(p,m,k,20))
+  rhoInit1 = array(0, dim = c(m,m,k,20))
+  piInit1 = matrix(0,20,k)
+  gamInit1 = array(0, dim=c(n,k,20))
+  LLFinit1 = list()
+  
+  require(MASS) #Moore-Penrose generalized inverse of matrix
+  for(repet in 1:20)
+  {
+    clusters = hclust(dist(y)) #default distance : euclidean
+    #cutree retourne les indices (? quel cluster indiv_i appartient) d'un clustering hierarchique
+    clusterCut = cutree(clusters,k)
+    Zinit1[,repet] = clusterCut
+    
+    for(r in 1:k)
+    {
+      Z = Zinit1[,repet]
+      Z_bin = vec_bin(Z,r)
+      Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r
+      Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+      
+      betaInit1[,,r,repet] =
+        ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+      sigmaInit1[,,r,repet] = diag(m)
+      phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
+      rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
+      piInit1[repet,r] = sum(Z_vec)/n
+    }
+    
+    for(i in 1:n)
+    {
+      for(r in 1:k)
+      {
+        dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
+          (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
+        Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
+      }
+      sumGamI = sum(gam[i,])
+      gamInit1[i,,repet]= Gam[i,] / sumGamI
+    }
+    
+    miniInit = 10
+    maxiInit = 11
+    
+    new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
+                    gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
+    LLFEessai = new_EMG$LLF
+    LLFinit1[repet] = LLFEessai[length(LLFEessai)]
+  }
+  
+  b = which.max(LLFinit1)
+  phiInit = phiInit1[,,,b]
+  rhoInit = rhoInit1[,,,b]
+  piInit = piInit1[b,]
+  gamInit = gamInit1[,,b]
+  
+  return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))

 }
 }

diff --git a/R/main.R b/R/main.R
index eab5e3f..2eec878 100644 (file)
--- a/R/main.R
+++ b/R/main.R
@@ -100,7 +100,7 @@ SelMix = setRefClass(
                        "computation of the regularization grid"
                        #(according to explicit formula given by EM algorithm)
 
                        "computation of the regularization grid"
                        #(according to explicit formula given by EM algorithm)
 

-                       gridLambda <<- grillelambda(sx.phiInit,sx.rhoInit,sx.piInit,sx.tauInit,sx.X,sx.Y,
+                       gridLambda <<- gridLambda(sx.phiInit,sx.rhoInit,sx.piInit,sx.tauInit,sx.X,sx.Y,

                                sx.gamma,sx.mini,sx.maxi,sx.eps);
                },
 
                                sx.gamma,sx.mini,sx.maxi,sx.eps);
                },
 

diff --git a/R/modelSelection.R b/R/modelSelection.R
new file mode 100644 (file)
index 0000000..8020daa
--- /dev/null
+++ b/R/modelSelection.R
@@ -0,0 +1,37 @@
+#' Among a collection of models, this function constructs a subcollection of models with
+#' models having strictly different dimensions, keeping the model which minimizes 
+#' the likelihood if there were several with the same dimension
+#'
+#' @param LLF a matrix, the first column corresponds to likelihoods for several models
+#'        the second column corresponds to the dimensions of the corresponding models.
+#'
+#' @return a list with indices, a vector of indices selected models, 
+#'         and D1, a vector of corresponding dimensions
+#' @export
+#'
+#' @examples
+modelSelection = function(LLF)
+{
+  D = LLF[,2]
+  D1 = unique(D)
+  
+  indices = rep(1, length(D1))
+  #select argmax MLE
+  if (length(D1)>2)
+  {
+    for (i in 1:length(D1))
+    {
+      A = c()
+      for (j in 1:length(D))
+      {
+        if(D[[j]]==D1[[i]])
+          a = c(a, LLF[j,1])
+      }
+      b = max(a)
+      #indices[i] : first indices of the binary vector where u_i ==1
+      indices[i] = which.max(vec_bin(LLF,b)[[1]])
+    }
+  }
+  
+  return (list(indices=indices,D1=D1))
+}

diff --git a/R/selectionindice.R b/R/selectionindice.R
deleted file mode 100644 (file)
index 97014b1..0000000
--- a/R/selectionindice.R
+++ /dev/null
@@ -1,28 +0,0 @@
-selectionindice = function(phi, seuil)
-{
-       dim_phi = dim(phi)
-       pp = dim_phi[1]
-       m = dim_phi[2]
-
-       A = matrix(0, pp, m)
-       B = matrix(0, pp, m)
-
-       for(j in 1:pp)
-       {
-               cpt1 = 0
-               cpt2 = 0
-               for(mm in 1:m)
-               {
-                       if(max(phi[j,mm,]) > seuil)
-                       {
-                               cpt1 = cpt1 + 1
-                               A[j,cpt] = mm
-                       } else
-                       {
-                               cpt2 = cpt2+1
-                               B[j, cpt2] = mm
-                       }
-               }
-       }
-       return (list(A,B))
-}

diff --git a/R/selectionmodele.R b/R/selectionmodele.R
deleted file mode 100644 (file)
index ed32731..0000000
--- a/R/selectionmodele.R
+++ /dev/null
@@ -1,45 +0,0 @@
-vec_bin = function(X,r)
-{
-       Z = c()
-       indice = c()
-
-       j = 1
-       for(i in 1:length(X))
-       {
-               if(X[i] == r)
-               {
-                       Z[i] = 1
-                       indice[j] = i
-                       j=j+1
-               } else
-                       Z[i] = 0
-       }
-
-       return (list(Z=Z,indice=indice))
-}
-
-selectionmodele = function(vraisemblance)
-{
-       D = vraimsemblance[,2]
-       D1 = unique(D)
-
-       indice = rep(1, length(D1))
-       #select argmax MLE
-       if (length(D1)>2)
-       {
-               for (i in 1:length(D1))
-               {
-                       A = c()
-                       for (j in 1:length(D))
-                       {
-                               if(D[[j]]==D1[[i]])
-                                       a = c(a, vraimsemblance[j,1])
-                       }
-                       b = max(a)
-                       #indice[i] : premier indice du vecteur binaire où u_i ==1
-                       indice[i] = which.max(vec_bin(vraimsemblance,b)[[1]])
-               }
-       }
-
-       return (list(indice=indice,D1=D1))
-}

diff --git a/R/suppressionmodelesegaux.R b/R/suppressionmodelesegaux.R
deleted file mode 100644 (file)
index a588062..0000000
--- a/R/suppressionmodelesegaux.R
+++ /dev/null
@@ -1,20 +0,0 @@
-suppressionmodelesegaux = function(B1,B2,glambda,rho,pi)
-{
-       ind = c()
-       for (j in 1:length(glambda))
-       {
-               for (ll in 1:(l-1))
-               {
-                       if(B1[,,l] == B1[,,ll])
-                               ind = c(ind, l)
-               }
-       }
-       ind = unique(ind)
-       B1 = B1[,,-ind]
-       glambda = glambda[-ind]
-       B2 = B2[,,-ind]
-       rho = rho[,,,-ind] 
-       pi = pi[,-ind]
-
-       return (list(B1=B1,B2=B2,glambda=glambda,ind=ind,rho=rho,pi=pi))
-}

diff --git a/R/suppressionmodelesegaux2.R b/R/suppressionmodelesegaux2.R
deleted file mode 100644 (file)
index 741793b..0000000
--- a/R/suppressionmodelesegaux2.R
+++ /dev/null
@@ -1,24 +0,0 @@
-suppressionmodelesegaux2 = function(B1,rho,pi)
-{
-       ind = c()
-       dim_B1 = dim(B1)
-       B2 = array(0,dim=c(dim_B1[1],dim_B1[2],dim_B1[3]))
-       nombreLambda=dim_B1[[2]]
-       glambda = rep(0,nombreLambda)
-
-       #for(j in 1:nombreLambda){
-       #       for(ll in 1:(l-1)){
-       #               if(B1[,,l] == B1[,,ll]){
-       #                       ind = c(ind, l)
-       #               }
-       #       }
-       #}
-       #ind = unique(ind)
-       #B1 = B1[,,-ind]
-       #rho = rho[,,,-ind] 
-       #pi = pi[,-ind]
-
-       suppressmodel = suppressionmodelesegaux(B1,B2,glambda,rho,pi)
-       return (list(B1 = suppressmodel$B1, ind = suppressmodel$B2,
-               rho = suppressmodel$rho, pi = suppressmodel$pi))
-}

diff --git a/R/vec_bin.R b/R/vec_bin.R
new file mode 100644 (file)
index 0000000..01dbfe1
--- /dev/null
+++ b/R/vec_bin.R
@@ -0,0 +1,23 @@
+#' A function needed in initSmallEM
+#'
+#' @param X vector with integer values
+#' @param r integer
+#'
+#' @return a list with Z (a binary vector of size the size of X) and indices where Z is equal to 1
+vec_bin = function(X,r)
+{
+  Z = rep(0,length(X))
+  indice = c()
+  j = 1
+  for (i in 1:length(X))
+  {
+    if(X[i] == r)
+    {
+      Z[i] = 1
+      indice[j] = i
+      j=j+1
+    } else
+      Z[i] = 0
+  }
+  return (list(Z=Z,indice=indice))
+}
\ No newline at end of file