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 {
  17   require("gridExtra")
  18   require("ggplot2")
  19   require("reshape2")
  20   require("cowplot")
  21
  22   K <- length(model$pi)
  23   ## regression matrices
  24   gReg <- list()
  25   for (r in 1:K)
  26   {
  27     Melt <- melt(t((model$phi[, , r])))
  28     gReg[[r]] <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) +
  29       geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
  30       midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster",
  31       r))
  32   }
  33   print(gReg)
  34
  35   ## Differences between two clusters
  36   if (comp)
  37   {
  38     if (is.na(k1) || is.na(k))
  39     {
  40       print("k1 and k2 must be integers, representing the clusters you want to compare")
  41     }
  42     Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
  43     gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + geom_tile() +
  44       scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
  45         space = "Lab") + ggtitle(paste("Difference between regression matrices in cluster",
  46       k1, "and", k2))
  47     print(gDiff)
  48
  49   }
  50
  51   ### Covariance matrices
  52   matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
  53   for (r in 1:K)
  54   {
  55     matCov[, r] <- diag(model$rho[, , r])
  56   }
  57   MeltCov <- melt(matCov)
  58   gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile() +
  59     scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
  60       space = "Lab") + ggtitle("Covariance matrices")
  61   print(gCov)
  62
  63   ### Proportions
  64   gam2 <- matrix(NA, ncol = K, nrow = n)
  65   for (i in 1:n)
  66   {
  67     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
  68   }
  69
  70   bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2)) +
  71     geom_boxplot() + theme(legend.position = "none") + background_grid(major = "xy",
  72     minor = "none")
  73   print(bp)
  74
  75   ### Mean in each cluster
  76   XY <- cbind(X, Y)
  77   XY_class <- list()
  78   meanPerClass <- matrix(0, ncol = K, nrow = dim(XY)[2])
  79   for (r in 1:K)
  80   {
  81     XY_class[[r]] <- XY[model$affec == r, ]
  82     if (sum(model$affec == r) == 1)
  83     {
  84       meanPerClass[, r] <- XY_class[[r]]
  85     } else
  86     {
  87       meanPerClass[, r] <- apply(XY_class[[r]], 2, mean)
  88     }
  89   }
  90   data <- data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K,
  91     each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
  92   g <- ggplot(data, aes(x = time, y = mean, group = cluster, color = cluster))
  93   print(g + geom_line(aes(linetype = cluster, color = cluster)) + geom_point(aes(color = cluster)) +
  94     ggtitle("Mean per cluster"))
  95
  96 }