X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fplot_valse.R;fp=pkg%2FR%2Fplot_valse.R;h=73188d276c896c234dfe00a10149a01ab6edef65;hp=3160067d73ab50a596e8f2224fe427a260a0933c;hb=1196a43d961a95abc18d3c8e777e9a4e8233e562;hpb=859c30ec72871f923da0498c14a94e67b0219875

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 3160067..73188d2 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -6,23 +6,24 @@
 #' @param Y matrix of responses (of size n*m)
 #' @param model the model constructed by valse procedure
 #' @param n sample size
-#' @return several plots
+#' @param comp TRUE to enable pairwise clusters comparison
+#' @param k1 index of the first cluster to be compared
+#' @param k2 index of the second cluster to be compared
+#'
+#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line geom_point scale_fill_gradient2 geom_boxplot theme
+#' @importFrom cowplot background_grid
+#' @importFrom reshape2 melt
 #'
 #' @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)) +
+    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))
   }
@@ -31,10 +32,10 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   ## Differences between two clusters
   if (comp)
   {
-    if (is.na(k1) || is.na(k))
+    if (is.na(k1) || is.na(k2))
       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))
+    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")
@@ -48,7 +49,7 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   for (r in 1:K)
     matCov[, r] <- diag(model$rho[, , r])
   MeltCov <- melt(matCov)
-  gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile()
+  gCov <- ggplot(data = MeltCov, aes(x = "Var1", y = "Var2", fill = "value")) + geom_tile()
     + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
       space = "Lab")
     + ggtitle("Covariance matrices")
@@ -59,7 +60,7 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   for (i in 1:n)
     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
 
-  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2))
+  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")
@@ -80,7 +81,7 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   }
   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"))
+  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"))
 }