+utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse")
#' Plot
#'
#' It is a function which plots relevant parameters
#' @param k1 index of the first cluster to be compared
#' @param k2 index of the second cluster to be compared
#'
-#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line geom_point scale_fill_gradient2 geom_boxplot theme
+#' @importFrom ggplot2 ggplot aes ggtitle geom_tile geom_line scale_fill_gradient2 geom_boxplot theme
#' @importFrom cowplot background_grid
#' @importFrom reshape2 melt
#'
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))
+ for (r in 1:K) {
+ Melt <- reshape2::melt(t((model$phi[, , r])))
+ gReg[[r]] <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value)) +
+ ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white",
+ midpoint = 0, space = "Lab") + ggplot2::ggtitle(paste("Regression matrices in cluster", r))
}
print(gReg)
## Differences between two clusters
- if (comp)
- {
+ if (comp) {
if (is.na(k1) || is.na(k2))
print("k1 and k2 must be integers, representing the clusters you want to compare")
- Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
- gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) +
- geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
- space = "Lab") + ggtitle(paste("Difference between regression matrices in cluster",
+ Melt <- reshape2::melt(t(model$phi[, , k1] - model$phi[, , k2]))
+ gDiff <- ggplot2::ggplot(data = Melt, ggplot2::aes(x = Var1, y = Var2, fill = value)) +
+ ggplot2::geom_tile() + ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+ space = "Lab") + ggplot2::ggtitle(paste("Difference between regression matrices in cluster",
k1, "and", k2))
print(gDiff)
}
matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
for (r in 1:K)
matCov[, r] <- diag(model$rho[, , r])
- MeltCov <- melt(matCov)
- gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile() +
- scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
- space = "Lab") + ggtitle("Covariance matrices (diag., one row per cluster)")
+ MeltCov <- reshape2::melt(matCov)
+ gCov <- ggplot2::ggplot(data = MeltCov, ggplot2::aes(x = Var1, y = Var2, fill = value)) + ggplot2::geom_tile() +
+ ggplot2::scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,
+ space = "Lab") + ggplot2::ggtitle("Covariance matrices (diag., one row per cluster)")
print(gCov)
### Proportions
- gam2 <- matrix(NA, ncol = K, nrow = n)
+ gam2 <- matrix(NA, ncol = 2, nrow = n)
for (i in 1:n)
gam2[i, ] <- c(model$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") +
- ggtitle("Assignment boxplot per cluster")
+ bp <- ggplot2::ggplot(data.frame(gam2), ggplot2::aes(x = X2, y = X1, color = X2, group = X2)) + ggplot2::geom_boxplot() +
+ ggplot2::theme(legend.position = "none") + cowplot::background_grid(major = "xy", minor = "none") +
+ ggplot2::ggtitle("Assignment boxplot per cluster")
print(bp)
}