Dear subscribers to morphmet,
With the deepest grief we must inform you of the sudden
death on June 13 of Prof. Dennis E. Slice,
holder of the fourth Rohlf Award for Excellence in Morphometrics
and tireless founder and moderator of this newsgroup,
who suffered a
Dear all,
I also want to comment on the recent bgPCA postings.
Andrea et al. and Fred are right that bgPCA produces ordination plots in
which two or more groups are discriminated more (i.e., the groups overlap
less) than they should, whenever p (number of variables) is large relative
to n
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
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
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
Dear morphometrics community,
Perhaps this call is of interest to some of you.
Best,
Philipp
At the Faculty of Life Sciences of the University of Vienna the position of
a "University Professor of Theoretical Evolutionary Biology" is to be
filled.
The advertised professorship shall cover
I think a few topics get mixed up here.
Of course, a sample can be too small to be representative (as in Andrea's
example), and one should think carefully about the measures to take. It is
also clear that an increase in sample size reduces standard errors of
statistical estimates, including
Adding more (semi)landmarks inevitably increases the spatial resolution and
thus allows one to capture finer anatomical details - whether relevant to
the biological question or not. This can be advantageous for the
reconstruction of shapes, especially when producing 3D morphs by warping
dense
As an exploratory technique, PCA makes no distributional assumptions; it is
used to explore the empirical distribution of the data. The sample does not
need to be balanced with regard to sex or other grouping variables, but
larger groups have a stronger effect on the PCA than smaller groups.
We seek a PhD student for a three-year position in Vienna, working on the
morphometrics of human pelvises in relation to childbirth and motherhood:
www.oeaw.ac.at/fileadmin/subsites/Jobs/Job_offer_VAMOS_PhD_position_1_.pdf
Best,
Philipp Mitteroecker
--
MORPHMET may be accessed via its
In the working group of Philipp Mitteroecker in the Department of
Theoretical Biology, University of Vienna, a two-year postdoc position and
a three-year Ph.D. position are vacant. We are searching for enthusiastic
persons, who are dedicated to interdisciplinary research in biology and
I would like to announce a new paper, of which the early view pdf is
already online.
Mitteroecker P, Cheverud JM, Pavlicev M (2016) Multivariate Analysis of
Genotype-Phenotype Association. Genetics
http://www.genetics.org/content/early/2016/02/18/genetics.115.181339
It offers an exploratory
As Michael described, the average shape configuration affects the sliding
when used as reference for the TPS; the final configurations thus are
sample-dependent. However, if the curves/surfaces are covered densely
enough by the semilandmarks (e.g., to avoid that a semilandmark can slide
away
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