I'd like to ask a question here that I've also asked on Biostar (stackexchange) and someone there forwarded me to this website. I was wondering how I could perform a Bray Curtis similarity clustering in R in which I show the similarity percentages on an inverted Y-axis and all tree nodes ending at 100% as I've shown in a dendogram:
At the moment I create my plot in the following way (using S17 Bray Curtis dissimilarity measure, which just scales regular Bray Curtis to 0-100%):
library(vegan) mat = 'some matrix' d = (1 - vegdist(mat, method="bray")) * 100 h = hclust(d) plot(h)
Inverting the Y-axis (with
ylim=c(100,80)) doesn't work. How can I create a dendogram as shown above from a distance matrix? Thanks for any help / advice!
Original question can be found on the Biostar website here