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, comp = FALSE, k1 = NA, k2 = NA){
  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   if (comp){
  32     if (is.na(k1) || is.na(k)){print('k1 and k2 must be integers, representing the clusters you want to compare')}
  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   }
  40
  41   ### Covariance matrices
  42   matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
  43   for (r in 1:K){
  44     matCov[,r] = diag(model$rho[,,r])
  45   }
  46   MeltCov =  melt(matCov)
  47   gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() +
  48     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
  49     ggtitle("Covariance matrices")
  50   print(gCov )
  51
  52   ### Proportions
  53   gam2 = matrix(NA, ncol = K, nrow = n)
  54   for (i in 1:n){
  55     gam2[i, ] = c(model$proba[i, model$affec[i]], model$affec[i])
  56   }
  57
  58   bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
  59     geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
  60   print(bp)
  61
  62   ### Mean in each cluster
  63   XY = cbind(X,Y)
  64   XY_class= list()
  65   meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
  66   for (r in 1:K){
  67     XY_class[[r]] = XY[model$affec == r, ]
  68     if (sum(model$affec==r) == 1){
  69       meanPerClass[,r] = XY_class[[r]]
  70     } else {
  71       meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
  72     }
  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 }