X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;h=0a6fa9e7bd2f57dc606cc3adb2e4b4b5cbd361ef;hp=2c74554899dfbeca72187047b7f6f1f393ae3fda;hb=4d9db27f0d1749e5577038dedbc5f4d0826f2772;hpb=a6b60f91ff8d798a3dcb7da6acbc03fba8a0459d

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 2c74554..0a6fa9e 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -2,6 +2,8 @@
 #'
 #' It is 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
@@ -10,7 +12,7 @@
 #'
 #' @export
 #'
-plot_valse = function(model,n){
+plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
   require("gridExtra")
   require("ggplot2")
   require("reshape2")
@@ -28,13 +30,15 @@ plot_valse = function(model,n){
   print(gReg)
   
   ## Differences between two clusters
-  k1 = 1
-  k2 = 2
-  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)
+  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)
@@ -47,32 +51,27 @@ plot_valse = function(model,n){
     ggtitle("Covariance matrices")
   print(gCov )
   
-  ### proportions
-  Gam = matrix(0, ncol = K, nrow = n)
-  gam  = Gam
-  for (i in 1:n){
-    for (r in 1:K){
-      sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
-      Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
-    }
-    gam[i,] = Gam[i,] / sum(Gam[i,])
-  }
-  affec = apply(gam, 1,which.max)
+  ### Proportions
   gam2 = matrix(NA, ncol = K, nrow = n)
   for (i in 1:n){
-    gam2[i, ] = c(gam[i, affec[i]], affec[i])
+    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")
-  print(bp )
+  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[affec == r, ]
-    meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
+    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))