pkg/R/plot_valse.R

   1 #' Plot
   2 #'
   3 #' It is a function which plots relevant parameters
   4 #'
   5 #' @param model the model constructed by valse procedure
   6 #' @param n sample size
   7 #' @return several plots
   8 #'
   9 #' @examples TODO
  10 #'
  11 #' @export
  12 #'
  13 plot_valse = function(model,n){
  14   require("gridExtra")
  15   require("ggplot2")
  16   require("reshape2")
  17   require("cowplot")
  18
  19   K = length(model$pi)
  20   ## regression matrices
  21   gReg = list()
  22   for (r in 1:K){
  23     Melt = melt(t((model$phi[,,r])))
  24     gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  25       scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  26       ggtitle(paste("Regression matrices in cluster",r))
  27   }
  28   print(gReg)
  29
  30   ## Differences between two clusters
  31   k1 = 1
  32   k2 = 2
  33   Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
  34   gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  35     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  36     ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
  37   print(gDiff)
  38
  39   ### Covariance matrices
  40   matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
  41   for (r in 1:K){
  42     matCov[,r] = diag(model$rho[,,r])
  43   }
  44   MeltCov =  melt(matCov)
  45   gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  46     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  47     ggtitle("Covariance matrices")
  48   print(gCov )
  49
  50   ### proportions
  51   Gam = matrix(0, ncol = K, nrow = n)
  52   gam  = Gam
  53   for (i in 1:n){
  54     for (r in 1:K){
  55       sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
  56       Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
  57     }
  58     gam[i,] = Gam[i,] / sum(Gam[i,])
  59   }
  60   affec = apply(gam, 1,which.max)
  61   gam2 = matrix(NA, ncol = K, nrow = n)
  62   for (i in 1:n){
  63     gam2[i, ] = c(gam[i, affec[i]], affec[i])
  64   }
  65   bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
  66     geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
  67   print(bp )
  68
  69   ### Mean in each cluster
  70   XY = cbind(X,Y)
  71   XY_class= list()
  72   meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
  73   for (r in 1:K){
  74     XY_class[[r]] = XY[affec == r, ]
  75     meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
  76   }
  77   data = data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2],K))
  78   g = ggplot(data, aes(x=time, y = mean, group = cluster, color = cluster))
  79   print(g + geom_line(aes(linetype=cluster, color=cluster))+  geom_point(aes(color=cluster)) + ggtitle('Mean per cluster'))
  80
  81 }