From: Benjamin Auder <benjamin.auder@somewhere>
Date: Tue, 6 Dec 2016 13:07:39 +0000 (+0100)
Subject: replace spaces by tabs
X-Git-Url: https://git.auder.net/doc/html/css/current/img/%7B%7B?a=commitdiff_plain;h=e166ed4e1370aa7961f0d8609936591cfc6808cc;p=valse.git

replace spaces by tabs
---

diff --git a/R/basicInitParameters.R b/R/basicInitParameters.R
index 9801229..3583e68 100644
--- a/R/basicInitParameters.R
+++ b/R/basicInitParameters.R
@@ -1,6 +1,6 @@
 #-----------------------------------------------------------------------
 #' Initialize the parameters in a basic way (zero for the conditional mean,
-#'  uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)
+#'	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
@@ -10,19 +10,19 @@
 #-----------------------------------------------------------------------
 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 (data = list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = 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
index 8b12077..29d8f10 100644
--- a/R/discardSimilarModels.R
+++ b/R/discardSimilarModels.R
@@ -7,25 +7,25 @@
 #' @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.
+#'	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))
+	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/generateIO.R b/R/generateIO.R
index 4527f08..5f19488 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,36 +1,36 @@
 #' Generate a sample of (X,Y) of size n
 #' @param covX covariance for covariates (of size p*p*K)
 #' @param covY covariance for the response vector (of size m*m*K)
-#' @param pi   proportion for each cluster
+#' @param pi	 proportion for each cluster
 #' @param beta regression matrix
-#' @param n    sample size
+#' @param n		sample size
 #' 
 #' @return list with X and Y
 #' @export
 #-----------------------------------------------------------------------
 generateIO = function(covX, covY, pi, beta, n)
 {
-  p = dim(covX)[1]
-  
-  m = dim(covY)[1]
-  k = dim(covY)[3]
-  
-  Y = matrix(0,n,m)
-  require(mvtnorm)
-  X = rmvnorm(n, mean = rep(0,p), sigma = covX)
-  
-  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))
+	p = dim(covX)[1]
+
+	m = dim(covY)[1]
+	k = dim(covY)[3]
+
+	Y = matrix(0,n,m)
+	require(mvtnorm)
+	X = rmvnorm(n, mean = rep(0,p), sigma = covX)
+
+	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 3613f2b..b0d748a 100644
--- a/R/generateIOdefault.R
+++ b/R/generateIOdefault.R
@@ -8,22 +8,22 @@
 #-----------------------------------------------------------------------
 generateIOdefault = function(n, p, m, k)
 {
-  covX = diag(p)
-  covY = array(0, dim=c(m,m,k))
-  for(r in 1:k)
-  {
-    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 = diag(p)
+	covY = array(0, dim=c(m,m,k))
+	for(r in 1:k)
+	{
+		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 7b82f63..2c66e4c 100644
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,31 +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 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
+#' @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",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
index 1adece6..7835430 100644
--- a/R/indicesSelection.R
+++ b/R/indicesSelection.R
@@ -4,33 +4,33 @@
 #' @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)
+#'					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))
+	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 8cfb7e8..5044a38 100644
--- a/R/initSmallEM.R
+++ b/R/initSmallEM.R
@@ -9,66 +9,66 @@
 #' @export
 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
-  require(mclust) # K-means with selection of K
-  for(repet in 1:20)
-  {
-    clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
-    Zinit1[,repet] = clusters$classification
-    
-    for(r in 1:k)
-    {
-      Z = Zinit1[,repet]
-      Z_bin = vec_bin(Z,r)
-      Z_vec = Z_bin$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))
+	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
+	require(mclust) # K-means with selection of K
+	for(repet in 1:20)
+	{
+		clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+		Zinit1[,repet] = clusters$classification
+		
+		for(r in 1:k)
+		{
+			Z = Zinit1[,repet]
+			Z_bin = vec_bin(Z,r)
+			Z_vec = Z_bin$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/modelSelection.R b/R/modelSelection.R
index 5a79bb6..bc7eeae 100644
--- a/R/modelSelection.R
+++ b/R/modelSelection.R
@@ -3,34 +3,34 @@
 #' 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.
+#'				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
+#'				 and D1, a vector of corresponding dimensions
 #' @export
 #'
 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))
+	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/vec_bin.R b/R/vec_bin.R
index 01dbfe1..ece7280 100644
--- a/R/vec_bin.R
+++ b/R/vec_bin.R
@@ -6,18 +6,18 @@
 #' @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))
+	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