On 05/11/2018 18:50, Diego Ardón wrote:
Captura de pantalla 2018-11-05 a la(s) 11.40.26.png
Thank you Mr. Fruciano. I had already made the DFA, but wasn't aware the
graphical output represented both groups (it certainly makes sense). I
have a couple of other questions regarding semi-landma
I'd like to respond to your question because it comes up so often.
As noted by Carmelo in the other posting, a large number of variables
relative to the number of cases can lead to statistical problems. But often
it does not.
In all analyses that treat each variable separately - including the
Philipp’s message below felt a little like a déjà vu moment. I checked the
Morphmet archives and sure enough, we had a similar thread back in late
May/Early June, 2017. Diego, you might want to check that thread, as a lot of
what was discussed is relevant to your current questions.
Cheers!
Mi
I agree only in part.
Whether or not semilandmarks "really are needed" may be hard to say
beforehand. If the signal is known well enough before the study, even a
single linear distance or distance ratio may suffice. In fact, most
geometric morphometric studies are characterized by an oversampli
I agree with Philipp but I would like to add that the way I think about the
justification for the sliding of semilandmarks is that if one were smart enough
to know exactly where the most meaningful locations are along some curve then
one should just place the points along the curve and computati
Yes, but doesn't that also add more covariance that wasn't there in
the first place?
Neither least squares nor minimum bending energy, that we minimize for
sliding, are biological models: they will reduce variance but will do
it in ways that are totally biologically arbitrary.
In the examples I sh
Perhaps, but Procrustes superimposition already adds lots of covariances also.
It is a bit tricky (meaning that I do not know of a good solution) to preserve
the "real" covariances and distinguish them from artifacts of fitting. GM works
well for testing differences among means of groups but stu
Andrea,
I am intrigued by your initial comment about adding covariance that was
apparently absent. I tend to think of the problem from the other perspective
of not accounting for covariance that should be present. As a thought
experiment (that could probably be simulated, and maybe I am not c
Indeed one of my favourite examples where semilandmarks are really
useful is a paper by Hublin, Gunz et al. (with apologies for the
inaccurate ref. and mixed up order of authors) where they manage to
classify as Neanderthal a piece of cranial vault found (I believe) in
Belgium and possibly in the s
Yes, it was always well known that sliding adds covariance but this is
irrelevant for most studies, especially for group mean comparisons and
shape regressions: the kind of studies for which GMM is most efficient, as
Jim noted.
If you consider the change of variance-covariance structure due to
Agreed. In addition, I think it’s important to note that, in the original
implementations of the sliding algorithm, semilandmarks were slid not along the
curve itself, but along tangents to the curve (= off the boundary outline). How
much distortion this induces is, of course, a function of how
Thanks to everyone who have replied, I sure have a lot of reading to do,
but overall I feel more comfortable about my data. I might end up playing
around with removing some semi-landmarks, figuring that it shouldn't affect
in much the outcome. I'll get back in case I find any other doubts.
Than
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