Remove arg n in plot_valse (deduce from X)

diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index dada0ef..1633821 100644 (file)
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -1,6 +1,6 @@
 #' EMGLLF
 #'
-#' Run a generalized EM algorithm developped for mixture of Gaussian regression 
+#' Run a generalized EM algorithm developped for mixture of Gaussian regression
 #' models with variable selection by an extension of the Lasso estimator (regularization parameter lambda).
 #' Reparametrization is done to ensure invariance by homothetic transformation.
 #' It returns a collection of models, varying the number of clusters and the sparsity in the regression mean.

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index fa66b3d..9531ae4 100644 (file)
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -1,6 +1,6 @@
 #' EMGrank
 #'
-#' Run an generalized EM algorithm developped for mixture of Gaussian regression 
+#' Run an generalized EM algorithm developped for mixture of Gaussian regression
 #' models with variable selection by an extension of the low rank estimator.
 #' Reparametrization is done to ensure invariance by homothetic transformation.
 #' It returns a collection of models, varying the number of clusters and the rank of the regression mean.
@@ -36,9 +36,9 @@ EMGrank <- function(Pi, Rho, mini, maxi, X, Y, eps, rank, fast)
 # Yes, we should use by-columns storage everywhere... [later!]
 matricize <- function(X)
 {
-  if (!is.matrix(X)) 
+  if (!is.matrix(X))
     return(t(as.matrix(X)))
-  return(X)
+  X
 }
 
 # R version - slow but easy to read
@@ -69,15 +69,15 @@ matricize <- function(X)
     for (r in 1:k)
     {
       Z_indice <- seq_len(n)[Z == r] #indices where Z == r
-      if (length(Z_indice) == 0) 
+      if (length(Z_indice) == 0)
         next
       # U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*% 
-                 crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
+      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*%
+               crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
       S <- s$d
       # Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
       # of the initial product
-      if (rank[r] < length(S)) 
+      if (rank[r] < length(S))
         S[(rank[r] + 1):length(S)] <- 0
       phi[, , r] <- s$u %*% diag(S) %*% t(s$v) %*% Rho[, , r]
     }
@@ -107,7 +107,7 @@ matricize <- function(X)
 
     # update distance parameter to check algorithm convergence (delta(phi, Phi))
     deltaPhi <- c(deltaPhi, max((abs(phi - Phi))/(1 + abs(phi)))) #TODO: explain?
-    if (length(deltaPhi) > deltaPhiBufferSize) 
+    if (length(deltaPhi) > deltaPhiBufferSize)
       deltaPhi <- deltaPhi[2:length(deltaPhi)]
     sumDeltaPhi <- sum(abs(deltaPhi))
 
@@ -115,5 +115,5 @@ matricize <- function(X)
     Phi <- phi
     ite <- ite + 1
   }
-  return(list(phi = phi, LLF = LLF))
+  list(phi = phi, LLF = LLF)
 }

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 487a4e1..10cb191 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -77,5 +77,5 @@ initSmallEM <- function(k, X, Y, fast)
   piInit <- piInit1[b, ]
   gamInit <- gamInit1[, , b]
 
-  return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
+  list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)
 }

diff --git a/pkg/R/main.R b/pkg/R/main.R
index d750fec..c74d7fb 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -148,7 +148,7 @@ runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1,
   modelSel$tableau <- tableauRecap
 
   if (plot)
-    print(plot_valse(X, Y, modelSel, n))
+    print(plot_valse(X, Y, modelSel))
 
   return(modelSel)
 }

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 73188d2..febc65c 100644 (file)
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -5,7 +5,6 @@
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
 #' @param model the model constructed by valse procedure
-#' @param n sample size
 #' @param comp TRUE to enable pairwise clusters comparison
 #' @param k1 index of the first cluster to be compared
 #' @param k2 index of the second cluster to be compared
@@ -15,8 +14,9 @@
 #' @importFrom reshape2 melt
 #'
 #' @export
-plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
 {
+  n <- nrow(X)
   K <- length(model$pi)
   ## regression matrices
   gReg <- list()

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index e08a941..99959ca 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,6 +1,6 @@
 #' selectVariables
 #'
-#' It is a function which constructs, for a given lambda, the sets for each cluster of relevant variables.
+#' For a given lambda, construct the sets of relevant variables for each cluster.
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
@@ -66,8 +66,8 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
   if (ncores > 1)
     parallel::stopCluster(cl)
 
-  print(out)
-  # Suppress models which are computed twice 
+  print(out) #DEBUG TRACE
+  # Suppress models which are computed twice
   # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)