Update plot_valse and add it to main.R

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 6ff15b3..2ae01e6 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -27,7 +27,7 @@
 #' @export
 valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
        eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
-       size_coll_mod=50, fast=TRUE, verbose=FALSE)
+       size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE)
 {
   p = dim(X)[2]
   m = dim(Y)[2]
@@ -137,6 +137,9 @@ print(tableauRecap)
 
   mod = as.character(tableauRecap[indModSel,1])
   listMod = as.integer(unlist(strsplit(mod, "[.]")))
+  if (plot){
+    print(plot_valse())
+  }
   models_list[[listMod[1]]][[listMod[2]]]
-  models_list
+  
 }

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index a8da583..7fdaa71 100644 (file)
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -1 +1,78 @@
-#TODO: reprendre les plots d'Emilie dans reports/...
+#' 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
new file mode 100644 (file)
index 0000000..05963c8
--- /dev/null
+++ b/pkg/R/plot_valse.R
@@ -0,0 +1,78 @@
+#' 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$phi[,,r])))
+    gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
+      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
+      ggtitle(paste("Regression matrices in cluster",r))
+  }
+  print(gReg)
+  
+  ## Differences between two clusters
+  k1 = 1
+  k2 = 2
+  Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
+  gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
+    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
+    ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
+  print(gDiff)
+  
+  ### Covariance matrices
+  matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
+  for (r in 1:K){
+    matCov[,r] = diag(model$rho[,,r])
+  }
+  MeltCov =  melt(matCov)
+  gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
+    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
+    ggtitle("Covariance matrices")
+  print(gCov )
+  
+  ### proportions
+  Gam = matrix(0, ncol = K, nrow = n)
+  gam  = Gam
+  for (i in 1:n){
+    for (r in 1:K){
+      sqNorm2 = sum( (Y[i,]%*%model$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")
+  print(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))
+  print(g + geom_line(aes(linetype=cluster, color=cluster))+  geom_point(aes(color=cluster)) + ggtitle('Mean per cluster'))
+  
+}
\ No newline at end of file

diff --git a/reports/essaiPlot.R b/reports/essaiPlot.R
index b000987..10b0e01 100644 (file)
--- a/reports/essaiPlot.R
+++ b/reports/essaiPlot.R
@@ -1,17 +1,30 @@
 ### Regression matrices
-model = res_valse
+model = Res
 K = dim(model$phi)[3]
 valMax = max(abs(model$phi))
 
 require(fields)
+
 if (K<4){
   par(mfrow = c(1,K))
-} else par(mfrow = c(2, (K+1)/2))
+} else op = par(mfrow = c(2, (K+1)/2))
+
+## Phi
 
 for (r in 1:K){
-  image.plot(t(abs(model$phi[,,r])), 
+  image.plot(t(abs(model$phi[,,r])),
              col=gray(rev(seq(0,64,length.out=65))/65),breaks=seq(0,valMax,length.out=66))
 }
+par(mfrow = c(1,K),oma = c(0,0,3,0))
+mtext("Regression matrices in each cluster", side=3, line=4, font=2, cex=2, col='red')
+
+par(mfrow = c(1,2), oma=c(0,0,3,0))
+for (i in 1:4) 
+  plot(runif(20), runif(20), 
+       main=paste("random plot (",i,")",sep=''))
+par(op)
+mtext("Four plots", 
+      side=3, line=4, font=2, cex=2, col='red')
 
 ### Zoom onto two classes we want to compare 
 kSel = c(1,2)
@@ -35,7 +48,7 @@ for (r in 1:K){
 Gam = matrix(0, ncol = K, nrow = n)
 gam  = Gam
 for (i in 1:n){
-  for (r in 1:k){
+  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])
   }