X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;h=ec2302d6ccf47f7ed95ec4cc42b3f9be6295e8ef;hp=0a6fa9e7bd2f57dc606cc3adb2e4b4b5cbd361ef;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=5965d116de1595372c8d34281551183fd3799038

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
deleted file mode 100644
index 0a6fa9e..0000000
--- a/pkg/R/plot_valse.R
+++ /dev/null
@@ -1,80 +0,0 @@
-#' Plot
-#'
-#' 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
-#'
-#' @examples TODO
-#'
-#' @export
-#'
-plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
-  require("gridExtra")
-  require("ggplot2")
-  require("reshape2")
-  require("cowplot")
-  
-  K = length(model$pi)
-  ## regression matrices
-  gReg = list()
-  for (r in 1:K){
-    Melt = melt(t((model$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))
-  }
-  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))
-    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")
-  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$affec[i])
-  }
-  
-  bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
-    geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
-  print(bp)
-  
-  ### Mean in each cluster
-  XY = cbind(X,Y)
-  XY_class= list()
-  meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
-  for (r in 1:K){
-    XY_class[[r]] = XY[model$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'))
-  
-}
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