X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;h=62070615dd8d4bf1c6258c6212e5ccac5eedae1b;hp=0a6fa9e7bd2f57dc606cc3adb2e4b4b5cbd361ef;hb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed;hpb=20d12623f4f395ba126570b3230fc80214191d8e

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 0a6fa9e..6207061 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -1,4 +1,4 @@
-#' Plot
+#' Plot 
 #'
 #' It is a function which plots relevant parameters
 #'
@@ -12,69 +12,85 @@
 #'
 #' @export
 #'
-plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
+plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+{
   require("gridExtra")
   require("ggplot2")
   require("reshape2")
   require("cowplot")
   
-  K = length(model$pi)
+  K <- length(model$pi)
   ## regression matrices
-  gReg = list()
-  for (r in 1:K){
-    Melt = melt(t((model$phi[,,r])))
-    gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-      ggtitle(paste("Regression matrices in cluster",r))
+  gReg <- list()
+  for (r in 1:K)
+  {
+    Melt <- melt(t((model$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))
   }
   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))
+  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))
     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])
+  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")
-  print(gCov )
+  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")
+  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$affec[i])
+  gam2 <- matrix(NA, ncol = K, nrow = n)
+  for (i in 1:n)
+  {
+    gam2[i, ] <- c(model$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()
-  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)
+  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'))
+  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"))
   
-}
\ No newline at end of file
+}