Closed drelo closed 1 year ago
Hi,
So there are two steps in your example:
Applying the function treespace
performs the principal co-ordinates analysis (equivalently "multi dimensional scaling" or "principal components analysis" - they are all equivalent here because the distances are Euclidean).
Then BIGscape$pco$li[,1]
, BIGscape$pco$li[,2]
and BIGscape$pco$li[,3]
are the first three principal components. In your example, they are also saved after the findGroves
function as
BIG.groves$treespace$pco$li[,1]
, BIG.groves$treespace$pco$li[,2]
and BIG.groves$treespace$pco$li[,3]
so yes, your example is a 3D plot where the co-ordinates of each point correspond to those of the first three principal components.
Applying the function findGroves
performs the hierarchical clustering, classifying the trees into 8 clusters in your example. The result of that is in BIG.groves$groups
. It's not clear to me if you are then colouring your 3D plot according to these 8 clusters. If you are, then the legend figure ought to say as much, but if the colouring is about something different then you don't need to mention the hierarchical clustering, and actually you probably don't need the findGroves
step at all.
Hope that's helpful, let me know if I can clarify anything further.
Cheers,
Michelle
Thanks for your help and clarification. Your first point clarified my issue.
Yeah, I used findGroves as a shortcut to use directly the plot3d part of the code, I just checked and I could have used BIGscape$pco$li
directly, thanks for the advice.
Cheers
Andrés
Dear all, I loaded several trees and plotted it with plotGrovesD3 and plot3d. My question is the following one, following this procedure...
The plot3d ends like a 3d visualization of principal components right? Or should I describe it as a form of hierarchical clustering? I was thinking about the legend figure and was worried that I got it wrong. Thanks
Andrés