Sorry for the delay. I’ve been up in the Tasmanian mountains all week.
Yes, I would agree that ‘congruence’ between a SSH result an a classification would be comforting. This is not necessarily easy to accomplish though. ‘Visual’ checks maybe ok on very small datasets. Classifying a (Euclidean) ultrametric matrix from SSH and comparing it with the original classification has its complications even so. For example, you would need to check how well each point has been handled by SSH. In comparing classification with SSH, I’d normally expect the classification to be more robust, unless the stress was VERY low (<~0.05).
At the moment, PATN doesn’t produce ultrametrics or an individual stress breakdown, but we could.
Congruence between beta=-0.1 and beta=-0.25 would be nice!
There is no doubt that situations will occur where higher negative beta values will produce a ‘better’ result, even if you know what truth is.
I will take another look at beta using simulation and see what I come up with. Any other user feedback on this issue would be warmly welcomed.