plot fonctionne

[valse.git] / pkg / R / plot_valse.R

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index febc65c..b47c7da 100644 (file)
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -23,7 +23,7 @@ plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
   for (r in 1:K)
   {
     Melt <- melt(t((model$phi[, , r])))
-    gReg[[r]] <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value")) +
+    gReg[[r]] <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value))  +
       geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
       midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
   }
@@ -35,11 +35,9 @@ plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
     if (is.na(k1) || is.na(k2))
       print("k1 and k2 must be integers, representing the clusters you want to compare")
     Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
-    gDiff <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value"))
-      + geom_tile()
-      + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
-        space = "Lab")
-      + ggtitle(paste("Difference between regression matrices in cluster",
+    gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + 
+      geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+        space = "Lab") + ggtitle(paste("Difference between regression matrices in cluster",
         k1, "and", k2))
     print(gDiff)
   }
@@ -49,10 +47,9 @@ plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
   for (r in 1:K)
     matCov[, r] <- diag(model$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")
+  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 (diag., one row per cluster)")
   print(gCov)
 
   ### Proportions
@@ -60,28 +57,8 @@ plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
   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 <- 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")  + 
+    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"))
 }