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! Mike > On Nov 6, 2018, at 5:33 AM, mitte...@univie.ac.at wrote: > > 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 > computation of mean shapes and shape regressions - the number of variables > does NOT matter! Also in principal component analysis (PCA) and between-group > PCA there is NO restriction on the number of variables. However, the > distribution of landmarks across the organism can influence the results. > E.g., if one part - say the face - is covered only by a few anatomical > landmarks, and another part - e.g., the neurocranium - by many semilandmarks, > the latter one will dominate PCA results. But this holds true for all kinds > of landmarks and variables, not only for semilandmarks. > > Analyses that involve the inversion of a covariance matrix - such as multiple > regression, CVA, relative eigenanalysis, reduced rank regression, and > parametric multivariate tests - require a clear excess of cases over > variables. In any truly multivariate setting (such as geometric > morphometrics), these analyses - if unavoidable - should ALWAYS be preceded > by some sort of variable reduction and/or factor analysis. Again, this is not > specific to semilandmarks. > > Partial least squares (PLS) is somewhat in-between these to groups. As shown > in Bookstein's 2016 paper, the singular values (maximal covariances) in PLS > can be strongly inflated if the number of variables is large compared to the > number of cases. The singular vectors, however, are more stable. > > Essentially, the number of semilandmarks should be determined based on the > anatomical details to be captured. More semilandmarks are not "harmful," > perhaps just a waste of time. > > Best, > > Philipp Mitteroecker > > > > > > > > Am Montag, 5. November 2018 18:52:57 UTC+1 schrieb Diego Ardón: > Good day everybody, I actually have twoI'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 > computation of mean shapes and shape regressions - the number of variables > does NOT matter! Also in principal component analysis (PCA) and between-group > PCA there is NO restriction on the number of variables. However, the > distribution of landmarks across the organism can influence the results. > E.g., if one part - say the face - is covered only by a few anatomical > landmarks, and another part - e.g., the neurocranium - by many semilandmarks, > the latter one will dominate PCA results. But this holds true for all kinds > of landmarks and variables, not only for semilandmarks. > > Analyses that involve the inversion of a covariance matrix - such as multiple > regression, CVA, relative eigenanalysis, reduced rank regression, and > parametric multivariate tests - require a clear excess of cases over > variables. In any truly multivariate setting (such as geometric > morphometrics), these analyses - if unavoidable - should ALWAYS be preceded > by some sort of variable reduction and/or factor analysis. Again, this is not > specific to semilandmarks. > > Partial least squares (PLS) is somewhat in-between these to groups. As shown > in Bookstein's 2016 paper, the singular values (maximal covariances) in PLS > can be strongly inflated if the number of variables is large compared to the > number of cases. The singular vectors, however, are more stable. > > Essentially, the number of semilandmarks should be determined based on the > anatomical details to be captured. More semilandmarks are not "harmful", > perhaps just a waste of time. > > Best, > > Philipp Mitteroecker > > > > > > questions here regarding semi-landmarks: > > So, I was adviced to use semi-landmarks, I placed them with MakeFan8, saved > the files as images and then used TpsDig to place all landmarks, however I > didn't make any distinctions between landmarks and semi-landmarks. What > unsettles me is (1) that I've recently comed across the term "sliding > semi-landmarks", which leads me to believe semi-landmarks should behave in a > particular way. > > The second thing that unsettles me is whether "more semi-landmarks" means a > better analysis. I can understand that most people wouldn't use 65 > landmarks+semilandmarks because it's a painstaking job to digitize them, > however, in my recent reads I've comed across concepts like a "Variables to > specimen ratio", which one paper suggested specimens should be 5 times the > number of variables. I do have a a data set of nearly 400 specimens, but it > does come short if indeed I should have 65*2*5 specimens! > > Please, I'll appreciate some feedback :) > > -- > MORPHMET may be accessed via its webpage at http://www.morphometrics.org > <http://www.morphometrics.org/> > --- > You received this message because you are subscribed to the Google Groups > "MORPHMET" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to morphmet+unsubscr...@morphometrics.org > <mailto:morphmet+unsubscr...@morphometrics.org>. -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org --- You received this message because you are subscribed to the Google Groups "MORPHMET" group. To unsubscribe from this group and stop receiving emails from it, send an email to morphmet+unsubscr...@morphometrics.org.
