+++ /dev/null
-#' 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"))
-}