-#' Plot
+#' Plot
#'
#' It is a function which plots relevant parameters
#'
#'
#' @export
#'
-plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
+plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+{
require("gridExtra")
require("ggplot2")
require("reshape2")
require("cowplot")
- K = length(model$pi)
+ K <- length(model$pi)
## regression matrices
- gReg = list()
- for (r in 1:K){
- Melt = melt(t((model$phi[,,r])))
- gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
- scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
- ggtitle(paste("Regression matrices in cluster",r))
+ gReg <- list()
+ for (r in 1:K)
+ {
+ Melt <- melt(t((model$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))
+ 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])
+ 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 )
+ 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$affec[i])
+ gam2 <- matrix(NA, ncol = K, nrow = n)
+ for (i in 1:n)
+ {
+ gam2[i, ] <- c(model$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")
+ 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)
+ 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'))
+ 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
+}