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 comp TRUE to enable pairwise clusters comparison
   9 #' @param k1 index of the first cluster to be compared
  10 #' @param k2 index of the second cluster to be compared
  11 #'
  12 #' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line geom_point scale_fill_gradient2 geom_boxplot theme
  13 #' @importFrom cowplot background_grid
  14 #' @importFrom reshape2 melt
  15 #'
  16 #' @export
  17 plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
  18 {
  19   n <- nrow(X)
  20   K <- length(model$pi)
  21   ## regression matrices
  22   gReg <- list()
  23   for (r in 1:K)
  24   {
  25     Melt <- melt(t((model$phi[, , r])))
  26     gReg[[r]] <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value")) +
  27       geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
  28       midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
  29   }
  30   print(gReg)
  31
  32   ## Differences between two clusters
  33   if (comp)
  34   {
  35     if (is.na(k1) || is.na(k2))
  36       print("k1 and k2 must be integers, representing the clusters you want to compare")
  37     Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
  38     gDiff <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value"))
  39       + geom_tile()
  40       + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
  41         space = "Lab")
  42       + ggtitle(paste("Difference between regression matrices in cluster",
  43         k1, "and", k2))
  44     print(gDiff)
  45   }
  46
  47   ### Covariance matrices
  48   matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
  49   for (r in 1:K)
  50     matCov[, r] <- diag(model$rho[, , r])
  51   MeltCov <- melt(matCov)
  52   gCov <- ggplot(data = MeltCov, aes(x = "Var1", y = "Var2", fill = "value")) + geom_tile()
  53     + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
  54       space = "Lab")
  55     + ggtitle("Covariance matrices")
  56   print(gCov)
  57
  58   ### Proportions
  59   gam2 <- matrix(NA, ncol = K, nrow = n)
  60   for (i in 1:n)
  61     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
  62
  63   bp <- ggplot(data.frame(gam2), aes(x = "X2", y = "X1", color = "X2", group = "X2"))
  64     + geom_boxplot()
  65     + theme(legend.position = "none")
  66     + background_grid(major = "xy", minor = "none")
  67   print(bp)
  68
  69   ### Mean in each cluster
  70   XY <- cbind(X, Y)
  71   XY_class <- list()
  72   meanPerClass <- matrix(0, ncol = K, nrow = dim(XY)[2])
  73   for (r in 1:K)
  74   {
  75     XY_class[[r]] <- XY[model$affec == r, ]
  76     if (sum(model$affec == r) == 1) {
  77       meanPerClass[, r] <- XY_class[[r]]
  78     } else {
  79       meanPerClass[, r] <- apply(XY_class[[r]], 2, mean)
  80     }
  81   }
  82   data <- data.frame(mean = as.vector(meanPerClass),
  83     cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
  84   g <- ggplot(data, aes(x = "time", y = "mean", group = "cluster", color = "cluster"))
  85   print(g + geom_line(aes(linetype = "cluster", color = "cluster"))
  86     + geom_point(aes(color = "cluster")) + ggtitle("Mean per cluster"))
  87 }