Christy Hipsley <[email protected]> ha scritto:
Sorry I should have been more clear - the CVA was done using individual
shapes, so n=161, and the bgPCA was on species means (the basic unit of my
study), so n=92. I did the CVA on the individuals so as not to have more
"groups" than variables and avoid false separation. I've seen your bat
paper and indeed thought of doing something similar. I just liked the CVA
because it showed very well the environmental gradient along which the
different cranial shapes fall.
One (e.g., a reviewer) might wonder if the pattern observed in CVA but
not in bwgPCA is due to CVA and not to the pattern being real. After
all, if the environmental gradient is in some way important, that can
be analysed/plotted directly (I really don't know enough to gauge if
this is feasible or not in your particular case, just saying)
Best,
Carmelo
P.S. Here, the issue is not demonizing CVA but, rather, understanding
what is it for and using it for the right purposes.
--
Carmelo Fruciano
Postdoctoral Fellow - Queensland University of Technology - Brisbane,
Australia
Honorary Fellow - University of Catania - Catania, Italy
e-mail [email protected]
http://www.fruciano.it/research/
On Tuesday, November 29, 2016 at 10:04:02 AM UTC+11, Christy Hipsley wrote:
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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