- 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 )
-
+ 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)
+