+++ /dev/null
-#' Plot
-#'
-#' It is a function which plots relevant parameters
-#'
-#' @param model the model constructed by valse procedure
-#' @param n sample size
-#' @return several plots
-#'
-#' @examples TODO
-#'
-#' @export
-#'
-plot_valse = function(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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