X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;h=0ef5f7256a6e7f7a3158274389593e1d3b67e0f3;hp=ec2302d6ccf47f7ed95ec4cc42b3f9be6295e8ef;hb=6af1d4897dbab92a7be05068e0e15823378965d9;hpb=228ee602a972fcac6177db0d539bf9d0c5fa477f

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index ec2302d..0ef5f72 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -1,47 +1,45 @@
-#' Plot 
+utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse")
+
+#' Plot
 #'
-#' It is a function which plots relevant parameters
+#' A function which plots relevant parameters.
 #'
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
 #' @param model the model constructed by valse procedure
-#' @param n sample size
-#' @return several plots
+#' @param comp TRUE to enable pairwise clusters comparison
+#' @param k1 index of the first cluster to be compared
+#' @param k2 index of the second cluster to be compared
 #'
-#' @examples TODO
+#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line scale_fill_gradient2 geom_boxplot theme
+#' @importFrom cowplot background_grid
+#' @importFrom reshape2 melt
 #'
-#' @export
+#' @return No return value (only plotting).
 #'
-plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+#' @export
+plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
 {
-  require("gridExtra")
-  require("ggplot2")
-  require("reshape2")
-  require("cowplot")
-
+  n <- nrow(X)
   K <- length(model$pi)
   ## regression matrices
   gReg <- list()
-  for (r in 1:K)
-  {
-    Melt <- melt(t((model$phi[, , r])))
-    gReg[[r]] <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + 
-      geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
-      midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
+  for (r in 1:K) {
+    Melt <- reshape2::melt(t((model$phi[, , r])))
+    gReg[[r]] <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value))  +
+      ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white",
+      midpoint = 0, space = "Lab") + ggplot2::ggtitle(paste("Regression matrices in cluster", r))
   }
   print(gReg)
 
   ## Differences between two clusters
-  if (comp)
-  {
-    if (is.na(k1) || is.na(k))
+  if (comp) {
+    if (is.na(k1) || is.na(k2))
       print("k1 and k2 must be integers, representing the clusters you want to compare")
-    Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
-    gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value))
-      + geom_tile()
-      + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
-        space = "Lab")
-      + ggtitle(paste("Difference between regression matrices in cluster", 
+    Melt <- reshape2::melt(t(model$phi[, , k1] - model$phi[, , k2]))
+    gDiff <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value)) + 
+      ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+        space = "Lab") + ggplot2::ggtitle(paste("Difference between regression matrices in cluster",
         k1, "and", k2))
     print(gDiff)
   }
@@ -50,40 +48,19 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
   for (r in 1:K)
     matCov[, r] <- diag(model$rho[, , r])
-  MeltCov <- melt(matCov)
-  gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile()
-    + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
-      space = "Lab")
-    + ggtitle("Covariance matrices")
+  MeltCov <- reshape2::melt(matCov)
+  gCov <- ggplot2::ggplot(data = MeltCov, ggplot2::aes(x = Var1, y = Var2, fill = value)) + ggplot2::geom_tile() +
+    ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+      space = "Lab") + ggplot2::ggtitle("Covariance matrices (diag., one row per cluster)")
   print(gCov)
 
   ### Proportions
-  gam2 <- matrix(NA, ncol = K, nrow = n)
+  gam2 <- matrix(NA, ncol = 2, nrow = n)
   for (i in 1:n)
     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
 
-  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2))
-    + geom_boxplot()
-    + theme(legend.position = "none")
-    + background_grid(major = "xy", minor = "none")
+  bp <- ggplot2::ggplot(data.frame(gam2), ggplot2::aes(x = X2, y = X1, color = X2, group = X2)) + ggplot2::geom_boxplot() +
+     ggplot2::theme(legend.position = "none") + cowplot::background_grid(major = "xy", minor = "none")  + 
+    ggplot2::ggtitle("Assignment boxplot per cluster")
   print(bp)
-
-  ### Mean in each cluster
-  XY <- cbind(X, Y)
-  XY_class <- list()
-  meanPerClass <- matrix(0, ncol = K, nrow = dim(XY)[2])
-  for (r in 1:K)
-  {
-    XY_class[[r]] <- XY[model$affec == r, ]
-    if (sum(model$affec == r) == 1) {
-      meanPerClass[, r] <- XY_class[[r]]
-    } else {
-      meanPerClass[, r] <- apply(XY_class[[r]], 2, mean)
-    }
-  }
-  data <- data.frame(mean = as.vector(meanPerClass),
-    cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
-  g <- ggplot(data, aes(x = time, y = mean, group = cluster, color = cluster))
-  print(g + geom_line(aes(linetype = cluster, color = cluster))
-    + geom_point(aes(color = cluster)) + ggtitle("Mean per cluster"))
 }