From: emilie <emilie@devijver.org>
Date: Wed, 12 Apr 2017 16:31:21 +0000 (+0200)
Subject: fix few things
X-Git-Url: https://git.auder.net/%3C?a=commitdiff_plain;h=5965d116de1595372c8d34281551183fd3799038;p=valse.git

fix few things
---

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index b864985..ac54319 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,6 +1,7 @@
 #' constructionModelesLassoMLE
 #'
-#' TODO: description
+#' Construct a collection of models with the Lasso-MLE procedure.
+#' 
 #'
 #' @param ...
 #'
diff --git a/pkg/R/main.R b/pkg/R/main.R
index 72ee724..6d315cd 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -148,7 +148,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
   modelSel$proba = Gam
   
   if (plot){
-    print(plot_valse(modelSel,n))
+    print(plot_valse(X,Y,modelSel,n))
   }
   
   return(modelSel)
diff --git a/pkg/R/plot.R b/pkg/R/plot.R
deleted file mode 100644
index 7fdaa71..0000000
--- a/pkg/R/plot.R
+++ /dev/null
@@ -1,78 +0,0 @@
-#' Plot
-#'
-#' It is a function which plots relevant parameters
-#'
-#'
-#' @return several plots
-#'
-#' @examples TODO
-#'
-#' @export
-#'
-plot_valse = function(){
-  require("gridExtra")
-  require("ggplot2")
-  require("reshape2")
-  
-  ## regression matrices
-  gReg = list()
-  for (r in 1:K){
-    Melt = melt(t((model$phi[,,r])))
-    gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-      ggtitle(paste("Regression matrices in cluster",r))
-  }
-  gReg
-  
-  ## Differences between two clusters
-  k1 = 1
-  k2 = 2
-  Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
-  gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-    ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
-  gDiff
-  
-  ### Covariance matrices
-  matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
-  for (r in 1:K){
-    matCov[,r] = diag(model$rho[,,r])
-  }
-  MeltCov =  melt(matCov)
-  gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-    ggtitle(paste("Regression matrices in cluster",r))
-  gCov 
-  
-  ### proportions
-  Gam = matrix(0, ncol = K, nrow = n)
-  gam  = Gam
-  for (i in 1:n){
-    for (r in 1:K){
-      sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
-      Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
-    }
-    gam[i,] = Gam[i,] / sum(Gam[i,])
-  }
-  affec = apply(gam, 1,which.max)
-  gam2 = matrix(NA, ncol = K, nrow = n)
-  for (i in 1:n){
-    gam2[i, ] = c(gam[i, affec[i]], affec[i])
-  }
-  bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
-    geom_boxplot() + theme(legend.position = "none")
-  bp + background_grid(major = "xy", minor = "none")
-  
-  ### Mean in each cluster
-  XY = cbind(X,Y)
-  XY_class= list()
-  meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
-  for (r in 1:K){
-    XY_class[[r]] = XY[affec == r, ]
-    meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
-  }
-  data = data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2],K))
-  g = ggplot(data, aes(x=time, y = mean, group = cluster, color = cluster))
-  g + geom_line(aes(linetype=cluster, color=cluster))+  geom_point(aes(color=cluster))
-  
-}
\ No newline at end of file
diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 0963946..0a6fa9e 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -2,6 +2,8 @@
 #'
 #' It is a function which plots relevant parameters
 #'
+#' @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
 #' @return several plots
@@ -10,7 +12,7 @@
 #'
 #' @export
 #'
-plot_valse = function(model,n, comp = FALSE, k1 = NA, k2 = NA){
+plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
   require("gridExtra")
   require("ggplot2")
   require("reshape2")
diff --git a/reports/simulData_17mars.R b/reports/simulData_17mars.R
index 52148fd..93a8f20 100644
--- a/reports/simulData_17mars.R
+++ b/reports/simulData_17mars.R
@@ -15,7 +15,7 @@ simulData_17mars = function(ite){
   beta = list()
   p1= 0.5
   beta[[1]] =diag(c(rep(p1,5),rep(1,5), rep(p1,5), rep(1, p-15)))
-  p2 = 1
+  p2 = 2
   beta[[2]] = diag(c(rep(p2,5),rep(1,5), rep(p2,5), rep(1, p-15)))
   ARI1 = ARI2 = ARI3 = 0
   
@@ -48,8 +48,8 @@ simulData_17mars = function(ite){
     XYproj[,i] = c(Ax,Dx,Ay,Dy)
   }
   
-  res_valse = valse(x,y, kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 200)
-  res_valse_proj = valse(XYproj[1:p,],XYproj[(p+1):(2*p),], kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 200)
+  res_valse = valse(t(x),t(y), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000)
+  res_valse_proj = valse(t(XYproj[1:p,]),t(XYproj[(p+1):(2*p),]), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000)
   
   save(res_valse,file=paste("Res_",ite, ".RData",sep=""))
   save(res_valse_proj,file=paste("ResProj_",ite, ".RData",sep=""))