Create a gist now

Instantly share code, notes, and snippets.

What would you like to do?
ks.default <- function(rows) seq(2, max(3, rows %/% 4))
many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) {
ldply(seq_along(ks), function(i) {
cl <- kmeans(x, centers = ks[i], ...)
data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster)
})
}
all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") {
d <- dist(x, method = point.dist)
cl <- hclust(d, method = cluster.dist)
ldply(seq_along(ks), function(i) {
data.frame(
obs = seq_len(nrow(x)), i = i, k = ks[i],
cluster = cutree(cl, ks[i])
)
})
}
center <- function(x) x - mean(range(x))
#' @param clusters data frame giving cluster assignments as produced by
#' many_kmeans or all_hclust
#' @param y value to plot on the y-axis. Should be length
#' \code{max(clusters$obs)}
clustergram <- function(clusters, y, line.width = NULL) {
clusters$y <- y[clusters$obs]
clusters$center <- ave(clusters$y, clusters$i, clusters$cluster)
if (is.null(line.width)) {
line.width <- 0.5 * diff(range(clusters$center, na.rm = TRUE)) /
length(unique(clusters$obs))
}
clusters$line.width <- line.width
# Adjust center positions so that they don't overlap
clusters <- clusters[with(clusters, order(i, center, y, obs)), ]
clusters <- ddply(clusters, c("i", "cluster"), transform,
adj = center + (line.width * center(seq_along(y)))
)
structure(clusters,
class = c("clustergram", class(clusters)),
line.width = line.width)
}
plot.clustergram <- function(x) {
i_pos <- !duplicated(x$i)
means <- ddply(x, c("cluster", "i"), summarise,
min = min(adj), max = max(adj))
ggplot(x, aes(i)) +
geom_ribbon(aes(y = adj, group = obs, fill = y, ymin = adj - line.width/2, ymax = adj + line.width/2, colour = y)) +
geom_errorbar(aes(ymin = min, ymax = max), data = means, width = 0.1) +
scale_x_continuous("cluster", breaks = x$i[i_pos], labels = x$k[i_pos]) +
labs(y = "Cluster average", colour = "Obs\nvalue", fill = "Obs\nvalue")
}
iris_s <- scale(iris[,-5])
k_def <- many_kmeans(iris_s)
k_10 <- many_kmeans(iris_s, 2:10)
k_rep <- many_kmeans(iris_s, rep(4, 5))
h_def <- all_hclust(iris_s)
h_10 <- all_hclust(iris_s, 2:10)
h_5 <- all_hclust(iris_s, seq(2, 20, by = 4))
pr <- princomp(iris_s)
pr1 <- predict(pr)[, 1]
pr2 <- predict(pr)[, 2]
plot(clustergram(k_def, pr1))
plot(clustergram(k_rep, pr1))
plot(clustergram(k_rep, pr2))
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment