RE: [MORPHMET] Trouble with 2-D morphomeric analysis in geomorph

2018-08-02 Thread Murat Maga 
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Re: [MORPHMET] curiosity about ancestral shape reconstruction

2018-08-02 Thread Polly, P. David 
My reply to Andrea the other day bounced from the list and Andrea only copied 
some of it in his reply so I'm reposting the whole thing in case anyone is 
interested.  (thanks Dennis Slice for helping me with the bounce).

Hi Andrea,

Using a Brownian motion model, ancestor reconstructions are essentially means 
of the tip taxa weighted by the branch lengths connecting them to the nodes.  
Branch length is arguably more important for the outcome than tree topology.  
In your example, B and C are likely to have much stronger influence on the 
reconstruction than A because of this.

Consider a scenario in which A, B, and C form a trichotomy and you are trying 
to reconstruct their ancestral trait value.  If they are all extant species 
they will contribute equally because their branch lengths will be the same, the 
ancestral estimate would therefore be the simple average of the three.  But if 
B and C are extinct, their branch lengths will be shorter and they will 
contribute proportionally more to the reconstruction.  If B and C lie very 
close in time to the node, A will have very little impact on the reconstruction.

Consider another scenario with relationships ((A,B), C) and you are 
reconstructing the ancestor of (A,B). If all three taxa are extant, A and B 
will have the strongest influence because the total branch length between C and 
the node of interest is longer.  However, if C lived only a short time after 
the base node and if the node (A,B) is deep (closer to the base of the tree 
than to its tips) then C will have a stronger influence on the ancestral 
reconstruction of node (A,B) than do the tip taxa A & B.

I think part of your question involves the morphology of B and C being poorly 
estimated (as they might be if you are using mean values of quantitative traits 
for the tips of A, B, and C).  These estimates are also important.  If B & C 
are badly estimated, they will bias the ancestor reconstruction.  If they are 
connected to the nodes by long branches relative to well estimated taxa, the 
effect will be minimal, but if they are connected by short branches their bias 
will overwhelm the contribution of better estimated but more distant taxa.

With best wishes,
David



P. David Polly
Robert R. Shrock Professor
Department of Earth and Atmospheric Sciences
Adjunct Professor, Biology and Anthropology
Indiana University
1001 E. 10th Street
Bloomington, IN  47405-1405
pdpo...@indiana.edu
+1 (812) 855-7994
http://pages.iu.edu/~pdpolly/

P. David Polly
Robert R. Shrock Professor
Department of Earth and Atmospheric Sciences
Adjunct Professor, Biology and Anthropology
Indiana University
1001 E. 10th Street
Bloomington, IN  47405-1405
pdpo...@indiana.edu
+1 (812) 855-7994
http://pages.iu.edu/~pdpolly/








On 30 Jul 2018, at 10:57 AM, Polly, P. David 
mailto:pdpo...@indiana.edu>> wrote:

Hi Andrea,

Using a Brownian motion model, ancestor reconstructions are essentially means 
of the tip taxa weighted by the branch lengths connecting them to the nodes.  
Branch length is arguably more important for the outcome than tree topology.  
In your example, B and C are likely to have much stronger influence on the 
reconstruction than A because of this.

Consider a scenario in which A, B, and C form a trichotomy and you are trying 
to reconstruct their ancestral trait value.  If they are all extant species 
they will contribute equally because their branch lengths will be the same, the 
ancestral estimate would therefore be the simple average of the three.  But if 
B and C are extinct, their branch lengths will be shorter and they will 
contribute proportionally more to the reconstruction.  If B and C lie very 
close in time to the node, A will have very little impact on the reconstruction.

Consider another scenario with relationships (A,B), C) and you are 
reconstructing the ancestor of (A,B). If all three taxa are extant, A and B 
will have the strongest influence because the total branch length between C and 
the node of interest is longer.  However, if C lived only a short time after 
the base node and if the node (A,B) is deep (closer to the base of the tree 
than to its tips) then C will have a stronger influence on the ancestral 
reconstruction of node (A,B) than do the tip taxa A & B.

I think part of your question involves the morphology of B and C being poorly 
estimated (as they might be if you are using mean values of quantitative traits 
for the tips of A, B, and C).  These estimates are also important.  If B & C 
are badly estimated, they will bias the ancestor reconstruction.  If they are 
connected to the nodes by long branches relative to well estimated taxa, the 
effect will be minimal, but if they are connected by short branches their bias 
will overwhelm the contribution of better estimated but more distant taxa.

With best wishes,
David



P. David Polly
Robert R. Shrock Professor
Department of Earth and Atmospheric Sciences

[MORPHMET] Trouble with 2-D morphomeric analysis in geomorph

2018-08-02 Thread Ian F 
I am trying to do an analysis of three specimens. I am using semi-landmarks and 
having trouble. perhaps someone can point out mt error? these are my results

Generalized Procrustes Analysis
with Partial Procrustes Superimposition

-4 fixed landmarks
12 semilandmarks (sliders)
2-dimensional landmarks
6 GPA iterations to converge
Minimized Bending Energy used


Consensus (mean) Configuration

   X   Y
[1,]  0.51447153 -0.03794928
[2,]  0.36957021  0.11431610
[3,] -0.11602250  0.19898458
[4,] -0.40489951  0.11095030
[5,] -0.43738229 -0.09965594
[6,] -0.20913543 -0.09698422
[7,]  0.04257626 -0.09131803
[8,]  0.29799009 -0.04811314

it should be three individuals with 4 landmarks and 4 sliders each. i want to 
perform PCA on the procrustes coordinates. my code and data are below. Im new 
to R and its probably an easy fix im just really confused. any help is greatly 
appreciated.  






Code:Y <- arrayspecs(coordsrformat, k=2, p= ncol(coordsrformat)/2)

slidermatrix

Y.gpa <- gpagen(Y,curves=slidermatrix)   
summary(Y.gpa)
plot(Y.gpa)

Y.gpa <- gpagen(Y,curves=slidermatrix,ProcD=FALSE )   
summary(Y.gpa)
plot(Y.gpa)
a<-(Y.gpa$coords)
plotTangentSpace(a) 




slider matrix:  

before  slide   after
5   6   7
7   8   1
1   2   3
3   4   5
1   8   7
7   6   5
5   4   3
3   2   1
1   2   3
3   4   5
5   6   7
7   8   1


coordsrformat:


163018271530154911981345908 1557833 1840
102718951223190714171887 
2767108126331581213920471693175315351159
184110672151100924371071 
2956928 29361692211223601300178413041016
1700888 2084832 2480872



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