require("ggplot2")
require("reshape2")
require("cowplot")
-
+
K <- length(model$pi)
## regression matrices
gReg <- list()
Melt <- melt(t((model$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))
+ 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))
+ 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")
+ 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$affec[i])
- }
-
- bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2)) +
- geom_boxplot() + theme(legend.position = "none") + background_grid(major = "xy",
- minor = "none")
+
+ bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2))
+ + geom_boxplot()
+ + theme(legend.position = "none")
+ + background_grid(major = "xy", minor = "none")
print(bp)
-
+
### Mean in each cluster
XY <- cbind(X, Y)
XY_class <- list()
for (r in 1:K)
{
XY_class[[r]] <- XY[model$affec == r, ]
- if (sum(model$affec == r) == 1)
- {
+ if (sum(model$affec == r) == 1) {
meanPerClass[, r] <- XY_class[[r]]
- } else
- {
+ } 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))
+ 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"))
-
+ print(g + geom_line(aes(linetype = cluster, color = cluster))
+ + geom_point(aes(color = cluster)) + ggtitle("Mean per cluster"))
}