Christy,
I would strongly advocate using visualizations of group means for describing
shape differences among groups as compared to CVA.
A Procrustes MANOVA provides the statistical inferences related to any
differences that might be present among groups, and the corresponding
thin-plate spline deformations of the overall mean to the group means provides
the shape deformations describing how groups may differ from that mean.
Visualizing multiple group means simultaneously in a figure, as is commonly
done in many morphometric studies, facilitates a visual language to describe
how they differ from the consensus, and thus how they differ from one another.
In geomorph, advanced.procD.lm provides the appropriate LS means for groups
from any linear model (manova, factorial manova, mancova, etc.). Shape
differences of these relative to the overall consensus shape as found using
‘mshape’ can then be generated using plotRefToTarget.
Hope this helps.
Dean
Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834
From: Christy Hipsley [mailto:chips...@museum.vic.gov.au]
Sent: Monday, November 28, 2016 5:04 PM
To: MORPHMET <morphmet@morphometrics.org>
Subject: [MORPHMET] the problem with CVA... or is it?
Dear Morphmet-ers,
I'm seeking advice on methods for visualizing shape features that distinguish
multiple groups using GM. I know CVA has fallen out of favor for a number of
reasons discussed here - e.g., more variables than groups, nonisotropic
variation:
Mitteroecker, P., and Bookstein, F. 2011. Linear discrimination, ordination,
and the visualization of selection gradients in modern morphometrics. Evol.
Biol. 38:100–114.
Klingenberg, C. P., and Monteiro, L. R. 2005. Distances and directions in
multidimensional shape spaces: Implications for morphometric applications.
Syst. Biol. 54:678–688.
Although given these limitations, is it really expected to give completely
false results regarding the visualization of shape changes? In my study sytem,
I show that ecological groups have statistically different cranial shapes,
using both Procrustes ANOVA and PGLS. Now I simply want to visualize what the
main features are that distinguish them, preferably using warps or wireframes,
so that those changes must be directly relateable to the original landmark
coordinates. I did that using individual specimens instead of species means, so
I have 161 individuals vs 144 variables (48 landmarks*3D). I also did a
between-group PCA on the species means which shows the same pattern, so is it
technically "wrong" to show both?
Thanks for any feedback on this issue, and I would appreciate to hear any
alternative methods that people might use. I use MorphoJ and Geomorph for
analyses.
Best,
Christy
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