Dear R-Users,
I have another question regarding trees (dendrograms).
After exploring the various hierarchical clustering methods, it seems that some
of the methods (average, single, median) add sequentially very small clusters
(even 1 leaf) to an increasingly larger branch.
I would like to quantify this more rigorously. I do not think that banner plots
fully capture this fact, as they are limited to height of the node where a leaf
binds.
I came up with 2 alternative measures:
- Ratio of leaves on 1 branch (larger branch) vs the other branch (see function
branch.ratios);
- Size of other branch of the node where 1 leaf binds;
The latter resembles the bannerplot; and is also limited only to nodes with
leaves.
Can anyone point me to such indexes in the literature and/or in other R
packages?
I am not an expert in the field. Searching for cluster indexes will likely
generate a huge number of false positive results (i.e. indexes for number of
clusters).
An example of this functionality is given below:
# Pre-computed Trees:
x1 = readRDS("Tree.Full.M_ward.D.rds")
x2 = readRDS("Tree.Full.M_average.rds")
br1 = branch.ratios(x1)
br2 = branch.ratios(x2)
# Alternative: size.leafBranch(x1);
par.old = par(mfrow = c(1,2))
hist(br1);
# Branch Ratio goes up to 1300!
hist(br2);
par(par.old)
# Note: Median & centroid are even more extreme!
The data sets and functions are on GitHub:
https://github.com/discoleo/PeptideClassifier/tree/main/inst/examples
Functions: branch.ratios, size.leafBranch, count.nodes;
https://github.com/discoleo/PeptideClassifier/blob/main/R/Helper.Tree.R
I have attached an image to this mail with all 8 histograms. The image is also
available on GitHub:
https://github.com/discoleo/PeptideClassifier/blob/main/Trees.BranchRatios.png
Many thanks in advance,
Leonard
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
