Hi Everybody,
Now I have a doubt that is more statistical than R's technical. Im working
with ecology of recent Foraminifera.
At the lab we used to perform cluster analysis using 1-Pearsons R and Wards
method (we already saw it in bibliography of the area) which renders good
results with our biological data. Recently, using R Software (vegan and
Cluster packages) which allows the combination of any kind of distances matrix
with any clustering method, we tried to used Bray Curtis + Wards (which seem to
be more appropriate to a matrix with a lot of zeros) and it renders a better
result. Furthermore, the results agree with our hypothesis and with the results
we have got with the Distance-based Redundancy Analysis - dbRDA or CAP. It
means, the analysis (Q-mode) clusters the stations according to the main
physical, sedimentary and biological characteristics of the study area.
We received some critical comments noticing that Wards Method accepts Euclidean
Distance only. So, we made the analysis again using Euclidean Distance but we
dont get the better results we had using 1-Pearsons R + Wards or Bray Curtis
+ Wards (actually any other distance + method combination rendered better
results). Trying to find answers in the specialized literature we just got
little more confused because in any moment we saw something like "You must use
it with Euclidean Distance" and like I said above we already saw in some
articles from respected journals, other kind of distance associated with the
Ward's Clustering method.
Is it wrong or is it non sense to do the analysis in the way we were doing?
The results with Wards combined with 1-Pearsons R or Bray Curtis fit better
with our hypothesis and have excellent agglomerative coefficients , but we
dont want to make inappropriate statistical procedures. I'm starting to
realize how powerful R is, but it doesn't justify doing nonsense statistics...
I hope one of you may help us!
Thank you in advance.
Rodrigo.
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