+#' Plot
+#'
+#' It is a function which plots relevant parameters
+#'
+#'
+#' @return several plots
+#'
+#' @examples TODO
+#'
+#' @export
+#'
+plot_valse = function(){
+ require("gridExtra")
+ require("ggplot2")
+ require("reshape2")
+
+ ## regression matrices
+ gReg = list()
+ for (r in 1:K){
+ Melt = melt(t((model$phi[,,r])))
+ gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
+ scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
+ ggtitle(paste("Regression matrices in cluster",r))
+ }
+ gReg
+
+ ## Differences between two clusters
+ k1 = 1
+ k2 = 2
+ Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
+ gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
+ scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
+ ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
+ gDiff
+
+ ### Covariance matrices
+ matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
+ for (r in 1:K){
+ matCov[,r] = diag(model$rho[,,r])
+ }
+ MeltCov = melt(matCov)
+ gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
+ scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
+ ggtitle(paste("Regression matrices in cluster",r))
+ gCov
+
+ ### proportions
+ Gam = matrix(0, ncol = K, nrow = n)
+ gam = Gam
+ for (i in 1:n){
+ for (r in 1:K){
+ sqNorm2 = sum( (Y[i,]%*%model$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)
+ gam2 = matrix(NA, ncol = K, nrow = n)
+ for (i in 1:n){
+ gam2[i, ] = c(gam[i, affec[i]], affec[i])
+ }
+ bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
+ geom_boxplot() + theme(legend.position = "none")
+ bp + background_grid(major = "xy", minor = "none")
+
+ ### 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)
+ }
+ 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))
+ g + geom_line(aes(linetype=cluster, color=cluster))+ geom_point(aes(color=cluster))
+
+}
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