X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;h=ec2302d6ccf47f7ed95ec4cc42b3f9be6295e8ef;hp=62070615dd8d4bf1c6258c6212e5ccac5eedae1b;hb=1b698c1619dbcf5b3a0608dc894d249945d2bce3;hpb=f7e157cdbcf2d60224c2d6773da9c698174e9aee

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 6207061..ec2302d 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -18,7 +18,7 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   require("ggplot2")
   require("reshape2")
   require("cowplot")
-  
+
   K <- length(model$pi)
   ## regression matrices
   gReg <- list()
@@ -27,51 +27,47 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
     Melt <- melt(t((model$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))
+      midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
   }
   print(gReg)
-  
+
   ## Differences between two clusters
   if (comp)
   {
     if (is.na(k1) || is.na(k))
-    {
       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", 
-      k1, "and", 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", 
+        k1, "and", k2))
     print(gDiff)
-    
   }
-  
+
   ### Covariance matrices
   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")
+  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")
   print(gCov)
-  
+
   ### Proportions
   gam2 <- matrix(NA, ncol = K, nrow = n)
   for (i in 1:n)
-  {
     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
-  }
-  
-  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2)) + 
-    geom_boxplot() + theme(legend.position = "none") + background_grid(major = "xy", 
-    minor = "none")
+
+  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2))
+    + geom_boxplot()
+    + theme(legend.position = "none")
+    + background_grid(major = "xy", minor = "none")
   print(bp)
-  
+
   ### Mean in each cluster
   XY <- cbind(X, Y)
   XY_class <- list()
@@ -79,18 +75,15 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   for (r in 1:K)
   {
     XY_class[[r]] <- XY[model$affec == r, ]
-    if (sum(model$affec == r) == 1)
-    {
+    if (sum(model$affec == r) == 1) {
       meanPerClass[, r] <- XY_class[[r]]
-    } else
-    {
+    } 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))
+  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"))
-  
+  print(g + geom_line(aes(linetype = cluster, color = cluster))
+    + geom_point(aes(color = cluster)) + ggtitle("Mean per cluster"))
 }