Re: data dimensionality question


Dear Lee,

Thanks for the email prompt.

My experience with SSH and other ordination methods is biased towards community compositional data, not the taxonomic interpretation of morphometrics (although I did give KYST a run in 1986 on the UTAS mainframe using morphometric data.).

I was interested in Mike Palmers take on this issue (

‘… the number of dimensions worth interpreting is usually very low.’ and ‘.. Third and higher axes can be constructed. The choice of ‘when to stop’ interpreting new axes is largely a matter of taste, the quantity and quality of the data, and the ability to interpret the results.’.

My experience suggests that looking at 2-3 dimensions is a pragmatic but reasonable start, and if the other interpretation statistics look problematic, think about the properties of the data and how you are treating it.

Lee I was interested in your comment about stress values ideally being <0.15, and putting aside how stress is measured, I have generally used a rule of thumb of 0.2, although for large data sets exceeding 1000 objects, this is often a real struggle.

I hesitate to say much more without a better understanding of the mathematics of ordination methods.

My vote is therefore for 2-3 dimensions.