Dear List,

I followed the discussion of the prior task "PSW and MDS" and it was very
interesting what Fred Bookstein replied to the outgoing posting regarding
the use of MDS:

"My rationale for using MDS instead of PCA is (1) uneven sample sizes /
missing data, (2) lack of replicate samples, (3) a more general interest
in overall patterns/distributions in the data."

Fred Bookstein replied:
"Your "rationale for using MDS" is actually the list of reasons for using
PCA, which in this specific context does PRECISELY what you are asking MDS
to do!"

Well, this brings up a question that I could not answer myself yet. Actually
I'm not a professional biologist, but I am very much interested in
morphometrics as part of systematic work.

I have a data set of a total of 70 specimens with eight morphological
characters recorded each. The number of specimens in each assumed OTU is not
equal though!
I have recorded ventrals, subcaudals, labials entering the eye, suboculars,
loreals, prefrontals, parietals, and the presence/absence of a specific
colour pattern (0= absent; 1=present).
Data were standard normalized (mean=0; st.deriv.=1). I initially used NMDS
to verify assumed OTUs and continued using PCoA.

The question now is, can I use these methods for these data to identify
morphological distinctions (identify new species/subspecies)?

Thanks in advance,
Wulf

---
The data set looks something like this:

[...]
267     65      3       0       1       1       1       1
259     67      2       1       1,5     2       1       0
258     69      3       0       1       1       1       0
269     70      3       0       2       2       1       0
263     62      3       0       2       2       1       0
271     68      3       0       2,5     2       1       0
274     67      3       0       3       2       1       0
265     63      3       0       2       2       1       0
278     72      3       0       1       1       2       1
278     71      3       0       1       1       2       1
277     70      3       0       1       1       2       1
273     70      3       0       1       1       2       0
270     71      3       0       1       1       2       0
267     65      3       0       1       1       1       0
269     64      3       0       1       1       1       0
[...]


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