#'
#' 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
+#' @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
#'
-#' @return several plots
-#'
-#' @examples TODO
+#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line geom_point scale_fill_gradient2 geom_boxplot theme
+#' @importFrom cowplot background_grid
+#' @importFrom reshape2 melt
#'
#' @export
-#'
-plot_valse = function(){
- require("gridExtra")
- require("ggplot2")
- require("reshape2")
-
+plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+{
+ 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$phi[, , r])))
+ gReg[[r]] <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value")) +
+ geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
+ midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
}
print(gReg)
-
+
## Differences between two clusters
- k1 = 1
- k2 = 2
- Melt = melt(t(model$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$rho[,,r])
- }
- MeltCov = melt(matCov)
- gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) + geom_tile() +
- scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, space = "Lab") +
- ggtitle("Covariance matrices")
- print(gCov )
-
- ### proportions
- Gam = matrix(0, ncol = K, nrow = n)
- gam = Gam
- for (i in 1:n){
- for (r in 1:K){
- sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
- Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
- }
- gam[i,] = Gam[i,] / sum(Gam[i,])
- }
- affec = apply(gam, 1,which.max)
- gam2 = matrix(NA, ncol = K, nrow = n)
- for (i in 1:n){
- gam2[i, ] = c(gam[i, affec[i]], affec[i])
+ if (comp)
+ {
+ if (is.na(k1) || is.na(k2))
+ print("k1 and k2 must be integers, representing the clusters you want to compare")
+ Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
+ gDiff <- ggplot(data = Melt, aes(x = "Var1", y = "Var2", fill = "value"))
+ + geom_tile()
+ + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+ space = "Lab")
+ + ggtitle(paste("Difference between regression matrices in cluster",
+ k1, "and", k2))
+ print(gDiff)
}
- bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
- geom_boxplot() + theme(legend.position = "none")
- print(bp + background_grid(major = "xy", minor = "none"))
-
+
+ ### Covariance matrices
+ matCov <- matrix(NA, nrow = dim(model$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[affec == r, ]
- 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'))
-
-}
\ No newline at end of file
+ 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"))
+}
projects / valse.git / blobdiff