RE: [MORPHMET] Re: the problem with CVA... or is it?

2016-11-29 Thread Adams, Dean [EEOBS] 
Christy,

If your model is: shape~eco.group, then the LS means for each ecological group 
can be extracted. These can then be examined visually by using TPS from the 
overall reference to each LS mean.

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 6:44 PM
To: MORPHMET <morphmet@morphometrics.org>
Subject: [MORPHMET] Re: the problem with CVA... or is it?

Dean - so in this case how would I use the LS means for each group (here I have 
only one factor and no slope) as the coordinates for the target specimen and 
the mean shape for all species as the reference?

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.

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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Re: [MORPHMET] Re: the problem with CVA... or is it?

2016-11-28 Thread Carmelo Fruciano 

Christy Hipsley  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 c.fruci...@unict.it
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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