Re: [MORPHMET] Curve sliding semilandmarks

[Stefan Schlager]

Be careful to dampen the displacement along the tangents, as there is no
surface to project the landmarks back to. Using Morpho, you can control
this using the parameter "stepsize".
Best
Stefan

On 06/12/16 19:21, Lawrence Fatica wrote:
>
> Hi all,
>
> I am working on a project examining shape variation in the pelvis. I
> used IDAV Landmark to place four curves of five semilandmarks each (as
> well as several fixed landmarks) along the major contours of the
> pelvis. I plan on doing the analysis in R, but I am unsure what my
> options are for semilandmarks sliding along curves. I am particularly
> concerned that the semilandmarks will slide along their tangents and
> off the bone when using sliding protocols that do not include the 3D
> mesh itself.
>
> Is this something I should be worried about? Has anyone else had
> success using sliding semilandmarks along curves?
>
>
> Thanks in advance for any insight,
>
> Lawrence
>
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Re: [MORPHMET] procD.allometry with group inclusion

[Mike Collyer]

Dear Tsung,

The procD.allometry function performs two basic processes when groups are 
provided.  First, it does a homogeneity of slopes (HOS) test.  This test 
ascertains whether two or more groups have parallel or unique slopes (the 
latter meaning at least one groups’s slope is different than the others).  The 
HOS test constructs two linear models: shape ~ size + group and shape ~ size + 
group + size:group, and performs an analysis of variance to determine if the 
size:group interaction significantly reduces the residual error produced.  
(Note: log(size) is a possible and default choice in this analysis.)

After this test, procD.allometry then provides an analysis of variance on each 
term in the resulting model from the HOS test.

Regarding your question, if the HOS test reveals there is significant 
heterogeneity in slopes, the coefficients returned allow one to find the unique 
linear equations, by group, which would be found from separate runs on 
procD.allometry, one group at a time.  If the HOS test reveals that there is 
not significant heterogeneity in slopes, the coefficients constrain the slopes 
for different groups to be the same (parallel).  

Finally, and I think more to your point, the projected regression scores are 
found by using for a (in the Xa calculation you note) the coefficients that 
represent a common or individual slope from the linear model produced.  The 
matrix of coefficients, B, is arranged as first row = intercept, second row = 
common slope, next rows (if applicable) are coefficients for the group factor 
(essentially change the intercept, by group), and finally, the last rows are 
the coefficients for the size:group interaction (if applicable), which change 
the common slope to match each group’s unique slope.  Irrespective of the 
complexity of this B matrix, a is found as the second row.  If you run 
procD.allometry group by group, it is the same as (1) asserting that group 
slopes are unique and (2) changing a to match not the common slope, but the 
summation of the common slope and the group-specific slope adjustment.  One 
could do that, but would lose the ability to compare the groups in the same 
plot, as each group would be projected on a different axis.  

Hope that helps.

Mike


> On Dec 8, 2016, at 3:37 AM, Tsung Fei Khang  wrote:
> 
> Hi all,
> 
> I would like to use procD.allometry to study allometry in two species. 
> 
> I understand that the function returns the regression score for each specimen 
> as Reg.proj, and that the calculation is obtained as:
> s = Xa, where X is the nxp matrix of Procrustes shape variables, and a is the 
> px1 vector of regression coefficients normalized to 1. I am able to verify 
> this computation from first principles when all samples are presumed to come 
> from the same species. 
> 
> However, what happens when we are interested in more than 1 species (say 2)? 
> I could run procD.allometry by including the species labels via f2=~gps, 
> where gps gives the species labels. Is there just 1 regression vector (which 
> feels weird, since this should be species-specific), or 2? If so, how can I 
> recover both vectors? What is the difference of including f2=~gps using all 
> data, compared to if we make two separate runs of procD.allometry, one for 
> samples from species 1, and another for samples from species 2?
> 
> Thanks for any help.
> 
> Rgds,
> 
> TF
> 
> 
> 
> 
> 
> 
> " PENAFIAN: E-mel ini dan apa-apa fail yang dikepilkan bersamanya ("Mesej") 
> adalah ditujukan hanya untuk kegunaan penerima(-penerima) yang termaklum di 
> atas dan mungkin mengandungi maklumat sulit. Anda dengan ini dimaklumkan 
> bahawa mengambil apa jua tindakan bersandarkan kepada, membuat penilaian, 
> mengulang hantar, menghebah, mengedar, mencetak, atau menyalin Mesej ini atau 
> sebahagian daripadanya oleh sesiapa selain daripada penerima(-penerima) yang 
> termaklum di atas adalah dilarang. Jika anda telah menerima Mesej ini kerana 
> kesilapan, anda mesti menghapuskan Mesej ini dengan segera dan memaklumkan 
> kepada penghantar Mesej ini menerusi balasan e-mel. Pendapat-pendapat, 
> rumusan-rumusan, dan sebarang maklumat lain di dalam Mesej ini yang tidak 
> berkait dengan urusan rasmi Universiti Malaya adalah difahami sebagai bukan 
> dikeluar atau diperakui oleh mana-mana pihak yang disebut.
> 
> 
> DISCLAIMER: This e-mail and any files transmitted with it ("Message") is 
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> confidential information. You are hereby notified that the taking of any 
> action in reliance upon, or any review, retransmission, dissemination, 
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> immediately and advise the sender by return e-mail. Opinions, conclusions and 
> other information in this Message tha

[MORPHMET] procD.allometry with group inclusion

[Tsung Fei Khang]

Hi all,

I would like to use procD.allometry to study allometry in two species. 

I understand that the function returns the regression score for each 
specimen as Reg.proj, and that the calculation is obtained as:
s = Xa, where X is the nxp matrix of Procrustes shape variables, and a is 
the px1 vector of regression coefficients normalized to 1. I am able to 
verify this computation from first principles when all samples are presumed 
to come from the same species. 

However, what happens when we are interested in more than 1 species (say 
2)? I could run procD.allometry by including the species labels via 
f2=~gps, where gps gives the species labels. Is there just 1 regression 
vector (which feels weird, since this should be species-specific), or 2? If 
so, how can I recover both vectors? What is the difference of including 
f2=~gps using all data, compared to if we make two separate runs of 
procD.allometry, one for samples from species 1, and another for samples 
from species 2?

Thanks for any help.

Rgds,

TF






-- 
" PENAFIAN: E-mel ini dan apa-apa fail yang dikepilkan bersamanya ("Mesej") 
adalah ditujukan hanya untuk kegunaan penerima(-penerima) yang termaklum di 
atas dan mungkin mengandungi maklumat sulit. Anda dengan ini dimaklumkan 
bahawa mengambil apa jua tindakan bersandarkan kepada, membuat penilaian, 
mengulang hantar, menghebah, mengedar, mencetak, atau menyalin Mesej ini 
atau sebahagian daripadanya oleh sesiapa selain daripada 
penerima(-penerima) yang termaklum di atas adalah dilarang. Jika anda telah 
menerima Mesej ini kerana kesilapan, anda mesti menghapuskan Mesej ini 
dengan segera dan memaklumkan kepada penghantar Mesej ini menerusi balasan 
e-mel. Pendapat-pendapat, rumusan-rumusan, dan sebarang maklumat lain di 
dalam Mesej ini yang tidak berkait dengan urusan rasmi Universiti Malaya 
adalah difahami sebagai bukan dikeluar atau diperakui oleh mana-mana pihak 
yang disebut.


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