Angus,
1) For that you just need general R-indexing to filter each
population, and then from that you can calculate the mean shape of
each population and make deformation grids to compare them (with
each other, or to the grand mean), e.g.
sh.pop1 <- shape[,,pop=="pop1"]
mean.pop1 <- mshape(sh.pop1)
grand.mean <- mshape(shape)
plotRefToTarget(grand.mean, mean.pop1)
This assumes you have a grouping object called pop, and (superimposed) shape data in a 3d-array called "shape"
2) If your objective is to review the shapes of each group, the approach above should be enough. For numerous reasons, I would recommend a PCA over a CVA plot for examining where these groups lie in morphospace and how much they overlap. For a detailed account on why this is the case, please read:
Klingenberg, C. P., and L. R. Monteiro. 2005. Distances and directions in multidimensional shape spaces: implications for morphometric applications. Syst. Biol. 54:678–688.
Mitteroecker, P., and F. L. Bookstein. 2011. Linear Discrimination, Ordination, and the Visualization of Selection Gradients in Modern Morphometrics. Evol. Biol. 38:100–114.
Hope this helps
Antigoni
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-- Antigoni Kaliontzopoulou Assistant Researcher CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos University of Porto Campus Agrário de Vairão, 4485-661 Vairão PORTUGAL https://cibio.up.pt/research-groups-1/details/phenevol http://cibio.up.pt/people/details/akaliont http://antigonik.wix.com/akaliontzopoulou http://www.seh-herpetology.org/
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Angus,
1) For that you just need general R-indexing to filter each population, and then from that you can calculate the mean shape of each population and make deformation grids to compare them (with each other, or to the grand mean), e.g.
sh.pop1 <- shape[,,pop=="pop1"]
mean.pop1 <- mshape(sh.pop1)
grand.mean <- mshape(shape)
plotRefToTarget(grand.mean, mean.pop1)
This assumes you have a grouping object called pop, and (superimposed) shape data in a 3d-array called "shape"
2) If your objective is to review the shapes of each group, the approach above should be enough. For numerous reasons, I would recommend a PCA over a CVA plot for examining where these groups lie in morphospace and how much they overlap. For a detailed account on why this is the case, please read:
Klingenberg, C. P., and L. R. Monteiro. 2005. Distances and directions in multidimensional shape spaces: implications for morphometric applications. Syst. Biol. 54:678–688.
Mitteroecker, P., and F. L. Bookstein. 2011. Linear Discrimination, Ordination, and the Visualization of Selection Gradients in Modern Morphometrics. Evol. Biol. 38:100–114.
Hope this helps
Antigoni
On 30/07/2018 05:38, angus lawrie wrote:
Hello everyone, I am working with different populations of snails which appear to have different morphologies. I have been using the plotreftotarget function to compare the deformations of the minimum / maximum of principal components 1 and 2 to understand where on my snails the difference in shape is mostly occurring.--I have two questions:1) I would like to generate populations specfic deformation grids (I have read advice from previous discussions to no success, cannot subset properly?)
2) I would like to use the populations' specific deformations grids on a CVA plot (attached) to demonstrate which populations have what general morphology.
Cheers,Angus
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> Antigoni Kaliontzopoulou
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> CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos
> University of Porto
> Campus Agrário de Vairão, 4485-661 Vairão
> PORTUGAL
> https://cibio.up.pt/research-groups-1/details/phenevolhttp://cibio.up.pt/people/details/akalionthttp://antigonik.wix.com/akaliontzopoulouhttp://www.seh-herpetology.org/
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