dendrogram

Given a tree diagram like the Dendrogram example ( source ), how would one put labels on the edges? The Javascript code to draw the edges looks like the next lines: var link = vis.selectAll("path.link") .data(cluster.links(nodes)) .enter().append("path") .attr("class", "link") .attr("d", diagonal); Mike Bostock, the author of D3, very graciously helped with the following solution. Define a style for g.link; I just copied the style for g.node. Then I replaced the "var link =...." code with the following. The x and y functions place the label in the center of the path. var linkg = vis.selectAll(

Following a previous post ( Label and color leaf dendrogram in r ) I have a follow-up question. My questions are similar to the post mentioned but I wonder can it be done using ape (e.g., plot(as.phylo(fit), type="fan", labelCol) as it has more type of phylogeny. The mentioned post questions were: How can I show the group codes in leaf label (instead of the sample number)? I wish to assign a color to each code group and colored the leaf label according to it (it might happen that they will not be in the same clade and by that I can find more information)? And the code sample is: sample = data

Is there an easy way to calculate lowest value of h in cut that produces groupings of a given minimum size? In this example, if I wanted clusters with at least ten members each, I should go with h = 3.80 : # using iris data simply for reproducible example data(iris) d <- data.frame(scale(iris[,1:4])) hc <- hclust(dist(d)) plot(hc) cut(as.dendrogram(hc), h=3.79) # produces 5 groups; group 4 has 7 members cut(as.dendrogram(hc), h=3.80) # produces 4 groups; no group has <10 members Since the heights of the splits are given in hc$height , I could create a set of candidate values using hc$height +

I know dendrograms are quite popular. However if there are quite large number of observations and classes it hard to follow. However sometime I feel that there should be better way to present the same thing. I got an idea but do not know how to implement it. Consider the following dendrogram. > data(mtcars) > plot(hclust(dist(mtcars))) Can plot it like a scatter plot. In which the distance between two points is plotted with line, while sperate clusters (assumed threshold) are colored and circle size is determined by value of some variable. You are describing a fairly typical way of going about