essai fusion

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

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
deleted file mode 100644 (file)
index ec2302d..0000000
--- a/pkg/R/plot_valse.R
+++ /dev/null
@@ -1,89 +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"))
-}