#'
#' @export
#'
-plot_valse = function(model,n){
+plot_valse = function(model,n, comp = FALSE, k1 = NA, k2 = NA){
require("gridExtra")
require("ggplot2")
require("reshape2")
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)
+ 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$rho[,,1])[1], ncol = K)
ggtitle("Covariance matrices")
print(gCov )
- ### proportions
+ ### Proportions
gam2 = matrix(NA, ncol = K, nrow = n)
for (i in 1:n){
- gam2[i, ] = c(model$Gam[i, model$affec[i]], model$affec[i])
+ gam2[i, ] = c(model$proba[i, model$affec[i]], model$affec[i])
}
bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
- print(bp )
+ print(bp)
### Mean in each cluster
XY = cbind(X,Y)
XY_class= list()
meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
for (r in 1:K){
- XY_class[[r]] = XY[affec == r, ]
- meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
+ XY_class[[r]] = XY[model$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))