+utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse")
+
#' Plot
#'
-#' It is a function which plots relevant parameters
+#' 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
+#' @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
#'
-#' @examples TODO
+#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line scale_fill_gradient2 geom_boxplot theme
+#' @importFrom cowplot background_grid
+#' @importFrom reshape2 melt
#'
-#' @export
+#' @return No return value (only plotting).
#'
-plot_valse = function(model,n){
- require("gridExtra")
- require("ggplot2")
- require("reshape2")
- require("cowplot")
-
- K = length(model$pi)
+#' @export
+plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
+{
+ n <- nrow(X)
+ K <- length(model$pi)
## regression matrices
- gReg = list()
- for (r in 1:K){
- Melt = melt(t((model$phi[,,r])))
- gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
- scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
- ggtitle(paste("Regression matrices in cluster",r))
+ gReg <- list()
+ for (r in 1:K) {
+ Melt <- reshape2::melt(t((model$phi[, , r])))
+ gReg[[r]] <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value)) +
+ ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white",
+ midpoint = 0, space = "Lab") + ggplot2::ggtitle(paste("Regression matrices in cluster", r))
}
print(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))
- 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])
+ if (comp) {
+ if (is.na(k1) || is.na(k2))
+ print("k1 and k2 must be integers, representing the clusters you want to compare")
+ Melt <- reshape2::melt(t(model$phi[, , k1] - model$phi[, , k2]))
+ gDiff <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value)) +
+ ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+ space = "Lab") + ggplot2::ggtitle(paste("Difference between regression matrices in cluster",
+ k1, "and", k2))
+ print(gDiff)
}
- 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$Gam[i, model$affec[i]], model$affec[i])
- }
-
- bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
- geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
- print(bp )
-
- ### Mean in each cluster
- XY = cbind(X,Y)
- XY_class= list()
- meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
- for (r in 1:K){
- XY_class[[r]] = XY[affec == r, ]
- meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
- }
- data = data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2],K))
- g = ggplot(data, aes(x=time, y = mean, group = cluster, color = cluster))
- print(g + geom_line(aes(linetype=cluster, color=cluster))+ geom_point(aes(color=cluster)) + ggtitle('Mean per cluster'))
-
-}
\ No newline at end of file
+
+ ### Covariance matrices
+ matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
+ for (r in 1:K)
+ matCov[, r] <- diag(model$rho[, , r])
+ MeltCov <- reshape2::melt(matCov)
+ gCov <- ggplot2::ggplot(data = MeltCov, ggplot2::aes(x = Var1, y = Var2, fill = value)) + ggplot2::geom_tile() +
+ ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+ space = "Lab") + ggplot2::ggtitle("Covariance matrices (diag., one row per cluster)")
+ print(gCov)
+
+ ### Proportions
+ gam2 <- matrix(NA, ncol = 2, nrow = n)
+ for (i in 1:n)
+ gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
+
+ bp <- ggplot2::ggplot(data.frame(gam2), ggplot2::aes(x = X2, y = X1, color = X2, group = X2)) + ggplot2::geom_boxplot() +
+ ggplot2::theme(legend.position = "none") + cowplot::background_grid(major = "xy", minor = "none") +
+ ggplot2::ggtitle("Assignment boxplot per cluster")
+ print(bp)
+}
projects / valse.git / blobdiff