pkg/R/plot_valse.R

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