pkg/R/plot_valse.R

   1 #' Plot
   2 #'
   3 #' It is a function which plots relevant parameters
   4 #'
   5 #'
   6 #' @return several plots
   7 #'
   8 #' @examples TODO
   9 #'
  10 #' @export
  11 #'
  12 plot_valse = function(){
  13   require("gridExtra")
  14   require("ggplot2")
  15   require("reshape2")
  16
  17   ## regression matrices
  18   gReg = list()
  19   for (r in 1:K){
  20     Melt = melt(t((model$phi[,,r])))
  21     gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  22       scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  23       ggtitle(paste("Regression matrices in cluster",r))
  24   }
  25   print(gReg)
  26
  27   ## Differences between two clusters
  28   k1 = 1
  29   k2 = 2
  30   Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
  31   gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  32     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  33     ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
  34   print(gDiff)
  35
  36   ### Covariance matrices
  37   matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
  38   for (r in 1:K){
  39     matCov[,r] = diag(model$rho[,,r])
  40   }
  41   MeltCov =  melt(matCov)
  42   gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  43     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  44     ggtitle("Covariance matrices")
  45   print(gCov )
  46
  47   ### proportions
  48   Gam = matrix(0, ncol = K, nrow = n)
  49   gam  = Gam
  50   for (i in 1:n){
  51     for (r in 1:K){
  52       sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
  53       Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
  54     }
  55     gam[i,] = Gam[i,] / sum(Gam[i,])
  56   }
  57   affec = apply(gam, 1,which.max)
  58   gam2 = matrix(NA, ncol = K, nrow = n)
  59   for (i in 1:n){
  60     gam2[i, ] = c(gam[i, affec[i]], affec[i])
  61   }
  62   bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
  63     geom_boxplot() + theme(legend.position = "none")
  64   print(bp + background_grid(major = "xy", minor = "none"))
  65
  66   ### Mean in each cluster
  67   XY = cbind(X,Y)
  68   XY_class= list()
  69   meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
  70   for (r in 1:K){
  71     XY_class[[r]] = XY[affec == r, ]
  72     meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
  73   }
  74   data = data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2],K))
  75   g = ggplot(data, aes(x=time, y = mean, group = cluster, color = cluster))
  76   print(g + geom_line(aes(linetype=cluster, color=cluster))+  geom_point(aes(color=cluster)) + ggtitle('Mean per cluster'))
  77
  78 }