I have three suggestions that may help. Reduce the point size Make the points highly transparent Downsample the points Since you do not provide any sample data, I will use some random data to ...

Here is a simple solution that you can compute yourself. It is trivial to solve your equation for $γ$ as a function of the other values. We get  γ = \sqrt{(\biggr( \frac{C}{v(ω)} \biggl) ^2 - {(ω^...

I think that you are mixing together two different things - random forests for regression and for classification. Regression means to predict a continuous value (number). Random forest can construct ...

Here is python code to make a pretty good match for your picture. from igraph import * AM = [[1,3,1,0], [3,7,3,0], [0,1,9,1], [0,1,3,1]] g = Graph.Weighted_Adjacency(AM) g.vs["color"] = ["red", "...

You say "I don't want to calculate slopes or averages by groups and cluster because the distributions don't seem linear, or normal" but it looks to me like each group makes a nice compact cluster ...

This is not a complete answer to your question, but I can explain at least part of the problem. Since you do not provide your data, I cannot reproduce your results. However, it is easy to demonstrate ...