Update plot_valse and add it to main.R

[valse.git] / pkg / R / plot.R

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))
+  
+}
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