- 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$affec[i])
- }
-
- bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
- geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
- print(bp )
-
+
+ ### Covariance matrices
+ matCov <- matrix(NA, nrow = dim(model$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)
+