[MORPHMET] Migrating to the New Morphmet (aka morphmet2)

[K. James Soda]

Hello Everyone,

Quick update on the state of morphmet: Over the weekend, I lost some
administrative control over the 2014 version of morphmet. Fearing that this
was an indication that it would go down soon, I started sending out
invitations for a new version. At this point, most members who were
registered on morphmet as of June 20, 2019 should have received an
invitation to the newest iteration; the exceptions are those members whose
email addresses are no longer valid for Google group purposes or have opted
out of invitations to Google groups. If you did not receive an invitation,
you can join the new list by emailing morphmet2+subscr...@googlegroups.com,
then follow the instructions of the subsequent emails.

If you are reading this message, then the 2014 version of morphmet is still
distributing mail. However, I am not certain if this distribution will
continue in the future. As such, I would encourage you to start using the
new version for your morphometric needs. To re-iterate the information in
the invitations:

1) To post a message, email morphm...@googlegroups.com.

2) To access the forum, go to https://groups.google.com/d/forum/morphmet2.

If you have any difficulties with any of these tasks, please feel free to
contact me on or off of the list (k.jamess...@gmail.com).

Sincerely,

James Soda
Interim Moderator
###
Assistant Professor
Department of Mathematics
Quinnipiac University
Hamden, CT 06518
###

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[MORPHMET] Morphmet in the Future

[K. James Soda]

Dear Fellow Morphometricians,

I want to give a quick comment about the future of morphmet after Dennis's
passing so that any necessary changes will not come as a surprise. Please
rest assure that it is not a question of whether morphmet will continue but
rather if any different procedures will be required to make or view posts.
Right now, morphmet is linked to morphometrics.org, which is a domain that
belonged to Dennis himself. As a result, it is possible that, at some point
in the future, morphmet may temporarily go down if the morphmetrics.org
domain becomes unavailable.

What does this mean for you as a member? Right now, very little. You can
continue to post to morphmet and new members can join morphmet without
interruption. If in the future, this ceases to be the case, you will
receive an invitation to a near identical Google group that will function
in the same manner as morphmet has operated since the 2014 transition.
Invitations will likely be distributed over the course of two to three
days. The invitation email will include instructions on any changes to the
submission process, which will likely only include a new email address for
making posts and a new web address for the forum.

If you have any questions or concerns, please contact me either on or off
of the list (k.jamess...@gmail.com).

Best regards,

James Soda


Interim Moderator
(Soon to be) Assistant Professor
Department of Mathematics
Quinnipiac University
Hamden, CT 06518


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Re: [MORPHMET] Dennis Slice

[K. James Soda]

Sent on behalf of Dean Adams:


Colleagues,



Like others I have spent the weekend grieving the passing of Dennis, and
trying to put my feelings of loss into words. Alas I cannot.



I’ve known Dennis for 25 years, since we overlapped in Jim’s lab in the
1990s. He was a constant fixture in our field, and his passing has left a
great hole. I will miss his positive attitude, his whit, his endless and
entertaining stories, and his life lessons. But mostly I will miss having
him in our community.



I will miss you, Dennis.



Dean







Dr. Dean C. Adams

Director of Graduate Education, EEB Program

Professor

Department of Ecology, Evolution, and Organismal Biology

Iowa State University

https://www.eeob.iastate.edu/faculty/adams/

phone: 515-294-3834

On Mon, Jun 17, 2019 at 3:53 AM K. James Soda  wrote:

> Hello Everyone,
>
> Since Friday afternoon, I have struggled to put into words the kind of
> adviser Dennis Slice was to his students to supplement the elegant
> testimonies to his abilities as a researcher, but words fail me. I do not
> know how I can properly portray the care and compassion that he afforded
> us. Despite his many accomplishments, he remained humble and never spoke
> down to us. He listened to our concerns with compassion and our ideas with
> an open mind. He treated us like family, and I know that I am not alone in
> saying that we felt the same way about him. Our weekly lab meetings and
> monthly lab dinners form some of my happiest memories of graduate school
> because he infused humor into our every interaction. He taught me so many
> things, both professionally (like people are more likely to read emails
> that are broken into smaller blocks) and personally (like a gentleman
> should always carry a handkerchief in case a lady cries). I can only hope
> that I as a professor will be able to foster curiosity and confidence in my
> students half as well as Dennis did in me.
>
> I deeply miss Dennis, and I suspect I always will.
>
> Thank you for your time,
>
> James Soda
>
> On Mon, Jun 17, 2019 at 3:19 AM Carmelo Fruciano 
> wrote:
>
>> This is extremely sad news. He has made enormous contributions to our
>> field and his figure will be greatly missed. I have had limited
>> interactions with him but, based on the interactions I had the privilege
>> to have, I also concur with Mauro Cavalcanti that he had a nice sense of
>> humor.
>> My most heartfelt condolences to his family, his friends, and all our
>> field.
>> Carmelo
>>
>>
>> --
>>
>>
>> ==
>> Carmelo Fruciano
>> Institute of Biology
>> Ecole Normale Superieure - Paris
>> CNRS
>> http://www.fruciano.it/research/
>>
>>
>> On 15/06/2019 16:55, mitte...@univie.ac.at wrote:
>> >   Dear subscribers to morphmet,
>> >
>> >   With the deepest grief we must inform you of the sudden
>> >   death on June 13 of Prof. Dennis E. Slice,
>> >   holder of the fourth Rohlf Award for Excellence in Morphometrics
>> >   and tireless founder and moderator of this newsgroup,
>> >   who suffered a heart attack in his home town of
>> >   Tallahassee, Florida. Morphometrics will not be the same
>> >   without him.
>> >
>> >  Jim Rohlf, Fred Bookstein, Paul O'Higgins,
>> >Benedikt Hallgrimsson, June 15, 2019
>> >
>> > --
>> > MORPHMET may be accessed via its webpage at
>> http://www.morphometrics.org
>> > ---
>> > To unsubscribe from this group and stop receiving emails from it, send
>> > an email to morphmet+unsubscr...@morphometrics.org
>> > <mailto:morphmet+unsubscr...@morphometrics.org>.
>>
>> --
>> MORPHMET may be accessed via its webpage at http://www.morphometrics.org
>> ---
>> To unsubscribe from this group and stop receiving emails from it, send an
>> email to morphmet+unsubscr...@morphometrics.org.
>>
>>

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Re: [MORPHMET] Dennis Slice

[K. James Soda]

Hello Everyone,

Since Friday afternoon, I have struggled to put into words the kind of
adviser Dennis Slice was to his students to supplement the elegant
testimonies to his abilities as a researcher, but words fail me. I do not
know how I can properly portray the care and compassion that he afforded
us. Despite his many accomplishments, he remained humble and never spoke
down to us. He listened to our concerns with compassion and our ideas with
an open mind. He treated us like family, and I know that I am not alone in
saying that we felt the same way about him. Our weekly lab meetings and
monthly lab dinners form some of my happiest memories of graduate school
because he infused humor into our every interaction. He taught me so many
things, both professionally (like people are more likely to read emails
that are broken into smaller blocks) and personally (like a gentleman
should always carry a handkerchief in case a lady cries). I can only hope
that I as a professor will be able to foster curiosity and confidence in my
students half as well as Dennis did in me.

I deeply miss Dennis, and I suspect I always will.

Thank you for your time,

James Soda

On Mon, Jun 17, 2019 at 3:19 AM Carmelo Fruciano 
wrote:

> This is extremely sad news. He has made enormous contributions to our
> field and his figure will be greatly missed. I have had limited
> interactions with him but, based on the interactions I had the privilege
> to have, I also concur with Mauro Cavalcanti that he had a nice sense of
> humor.
> My most heartfelt condolences to his family, his friends, and all our
> field.
> Carmelo
>
>
> --
>
>
> ==
> Carmelo Fruciano
> Institute of Biology
> Ecole Normale Superieure - Paris
> CNRS
> http://www.fruciano.it/research/
>
>
> On 15/06/2019 16:55, mitte...@univie.ac.at wrote:
> >   Dear subscribers to morphmet,
> >
> >   With the deepest grief we must inform you of the sudden
> >   death on June 13 of Prof. Dennis E. Slice,
> >   holder of the fourth Rohlf Award for Excellence in Morphometrics
> >   and tireless founder and moderator of this newsgroup,
> >   who suffered a heart attack in his home town of
> >   Tallahassee, Florida. Morphometrics will not be the same
> >   without him.
> >
> >  Jim Rohlf, Fred Bookstein, Paul O'Higgins,
> >Benedikt Hallgrimsson, June 15, 2019
> >
> > --
> > MORPHMET may be accessed via its webpage at http://www.morphometrics.org
> > ---
> > To unsubscribe from this group and stop receiving emails from it, send
> > an email to morphmet+unsubscr...@morphometrics.org
> > .
>
> --
> MORPHMET may be accessed via its webpage at http://www.morphometrics.org
> ---
> To unsubscribe from this group and stop receiving emails from it, send an
> email to morphmet+unsubscr...@morphometrics.org.
>
>

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Re: [MORPHMET] A question regarding "target shape"

[K. James Soda]

Dear Mr. Ardón,

Good question. Whenever we make shape comparisons in GM, be that via 
displacement vector or deformation grid (which is what you’re doing), we can 
typically only compare two shapes at a time. One shape is called the reference 
(or starting shape, in this case). This is the shape for which the grid would 
look “normal”; straight, equally spaced grid lines. The second is the target, 
where the grid is deformed to take this second configuration. If you want to 
compare two geographic groups, I would suggest setting one group’s mean shape 
to be the starting shape and the other group’s to the target; this will lead to 
the most direct comparison. I am not certain how easy this is to do in MorphoJ, 
though.

Hope this helps,

James

> On Oct 31, 2018, at 12:01 PM, Diego Ardón  wrote:
> 
> Hello, my name is Diego and I'm currently undertaking a Master's program in 
> Mexico. One of my thesis project involves a geometric morphometrics study on 
> the shape of a freshwater fish which distributes across Central America. I'm 
> currently having trouble with a concept that will probably be very simple to 
> most of you, but which I haven't found a way to get my head around. 
> 
> I'm running a CVA on MorphoJ, dividing my dataset into two geographically 
> distinct groups. I run the test and change the type of graph to a "Warped 
> Outline Drawing". So now the graph is showing a "starting shape" which I 
> interpret as it being the average of all my landmark data (both geographical 
> groupings), however I'm not sure on how to interpret the "target shape". I 
> was expecting to have two "target shapes", one for each of the geographical 
> groupings. Could someone please help point out my misunderstanding and offer 
> me a way on how to interpret the "target shape"? 
> 
> Thank you, I'll be very thankful
> 
> Diego Ardón
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Re: [MORPHMET] Pillai Tr

[K. James Soda]

Dear Dr. Rodriguez,

When you said Goodman, did you mean Goodall?  If so, these statistics arise
from two different types of tests. Both have their advantages and their
drawbacks.

Goodall's F-test is a shape-specific test for differences between groups,
if I am not mistaken. The advantage is that it was created specifically for
shape comparisons. The drawback, or so I understand, is that it makes some
rather stringent assumptions about how landmarks vary, which are not often
met in biological data. As a result, it has declined in popularity, at
least among morphmetricians.

Pillai's trace (pillai tra) is one of the statistics associated with a
MANOVA test, which is the multivariate analogue to the classical ANOVA. The
advantage is that this test has solid theoretical underpinnings and is well
accepted among statisticians, but the drawback is that it assumes
multivariate normality, which is not particularly common in
Procrustes-aligned data.

In my professional opinion (and others on the list may disagree), if I had
to choose between the two, I would favor Pillai's trace. That being said, I
would actually recommend a third option, a permutation test. Because
permutation tests make less distributional assumptions, they are often
better suited to Procrustes-aligned data. As a result, they have
experienced increased popularity among practitioners. What software are you
using? Many morphometric applications now include permutation tests.

Hope you found some of this useful,

James Soda
##
Post-Doctoral Researcher
University of Notre Dame
Notre Dame, IN USA
##

On Thu, Aug 23, 2018 at 10:44 AM Laura Rodríguez 
wrote:

> Hello everyone
>
> I would like to know what really Pillai tr means ... sometimes, p values
> for Goodman is lower than 0.05, but pillai tr p value is bigger than 0.05,
> and then I do not know if the difference is significant or not.
>
> Can someone please help me?
>
> Thank you
>
> L
> --
>  
> <
> http://www.facebook.com/pages/Universidad-Internacional-Isabel-I-de-Castilla/276653549082230>
>
>  
>
>
>
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Re: [MORPHMET] How to project shape difference onto different PC

[K. James Soda]

Dear Dr. Hu,

Let me begin by restating how I understand the question: You have completed
a PCA on a morphological data set in which there are two subsets of
interest. Now you would like to decompose the difference between the two
subsets into differences along individual PCs. Here is my two cents on the
issue:

I would say that the literal solution to this problem would probably be
something along the lines of what you proposed. For simplicity, say that
you summarized each subset using its mean position in the PC space. This
would be expressed as a vector where each element is a position along a
single PC. The difference between these two vectors would then be a
decomposition of how far you would need to move along each PC axis to move
from one mean to the other. You could then standardize the elements so that
their absolute values sum to one. This would be an expression of what
percentage of the distance is along each PC.

What I perceive as the subtext of your question, though, is whether this
sort of decomposition has a reasonable interpretation, and the answer to
this question is somewhat trickier. Assuming this is a GM data set, the
more relevant point might be how you convert the difference into
visualizations. A nice feature of GM data is that each PC will correspond
to a "type" of deformation. This feature can be used to decompose the
difference between two shapes in a shape-PC space as well. For example,
imagine you moved from one mean shape in the PC space to the other by only
moving parallel to PC axes. If you are interested in two PCs, this could be
accomplished in two ways. You could then visualize the shape at the points
where you make a turn; that is, you would visualize how mean shape 1 would
need to be deformed to have the same PC1 or PC2 score as mean shape 2 if
all other PCs were held constant. The degree of deformation would then
provide a qualitative measure of how radical each PC's contribution is to
the shape difference. Of course, this is not a quantitative measure, as you
requested, but I would argue it is a more helpful assessment b/c it
directly corresponds to observable phenomena. How helpful, though, will
depend on your research question.

Hope something in there helps a little,

James

On Thu, May 17, 2018 at 10:15 AM, Yinan Hu  wrote:

> Dear colleagues,
>
> I'm trying to figure out how to break down shape differences onto
> individual PC axes.  I have a morphospace where PC1 explains 60% of shape
> variation and PC2 explains 20% of variation. Two subsets of samples of
> particular interest do not differ much along PC1, but differs significantly
> along PC2. How should I project the shape difference between these subsets
> onto seperate PC axes, such that I can quantitatively show X% of shape
> difference between them are along PC1 and Y% is along PC2?
>
> A simple vector projection (i.e. using the mean difference of PC1 score
> and PC2 score) doesn't feel right to me as I don't think PC scores are
> directly comparable between different PCs. Or am I wrong?
> Any suggestions would be greatly appreciated.
>
> Many thanks for your time.
>
> --
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Re: [MORPHMET] How can I test the project for identification of unknown crania?

[K. James Soda]

Dear Dr. Ibrahim,

As you note, standard LDA does not usually permit nested groupings.  In
this scenario, you would either need to have one LDA for sex and one LDA
for ancestry OR you could have one LDA that classifies into compound
sex-ancestry groups (i.e., male-Malay, female-Malay, male-Chinese,
female-Chinese, male-Indian, and female-Indian). Obviously, the latter
option doubles the number of groups, which could have set backs depending
on the smallest sample size of any sex-ancestry combination.

If you are feeling adventurous, my advisor, a collaborator, and I have had
some success in classification problems with GM data that use two
sequential discriminate function analyses (see this paper:
https://onlinelibrary.wiley.com/doi/full/10.1002/jmor.20626, which uses a
comparable procedure to this paper:
http://digitallibrary.amnh.org/bitstream/handle/2246/4942/N3109.pdf?sequence=1).
We did this to decide whether a specimen had enough information content to
classify it to species, but a similar procedure could apply here. You would
need three LDAs: one that classifies the specimen to sex, one that
classifies male specimens to ancestry, and one that classifies female
specimens to ancestry. To classify a test specimen, you would classify the
individual to sex using the first LDA, then, based on this classification,
you would classify to ancestry using the appropriate sex-specific LDA.

Hope something in my ramblings helps you out,

James

On Mon, Apr 2, 2018 at 12:41 PM, Dr.Abdelnasser Ibrahim <
dr.nasse...@gmail.com> wrote:

> Dear Dr. Murat Maga,
>
> Thank you very much for your cooperation. It was very helpful.
>
> You already have 400 cases that you know the ancestry and sex, and you
> want to use this database to infer ancestry of two skulls, for which you
> already know the sex. All 400 cases, as well as your two unknown, specimens
> have associated landmark data that were landmarked in using identical
> protocols. Does that sum it up accurately? yes, this is right.
>
> I will derive LDA for sexual dimorphism but how can I derive it
> for ancestry? as there are three ancestries; Malay, Chinese and Indian.
>
> Regards,
> Abdelnasser
>
> On Mon, Apr 2, 2018 at 1:21 PM, Murat Maga  wrote:
>
>> We need to clarify a few things:
>>
>>
>>
>> You already have 400 cases that you know the ancestry and sex, and you
>> want to use this database to infer ancestry of two skulls, for which you
>> already know the sex. All 400 cases, as well as your two unknown, specimens
>> have associated landmark data that were landmarked in using identical
>> protocols. Does that sum it up accurately?
>>
>>
>>
>> If that’s the case, one solution would be to use derive a linear
>> discriminant function, and see how well it accurately identifies your
>> ‘known’ data. You can use all your landmark coordinates, your full (or
>> partial) PCA results to derive the LDA. At this point your goal would be to
>> validate the model. You have a large sample size and you can split half to
>> derive the LD function, and the remaining to test the LD (i.e., treat them
>> as unknowns). Once you a find a model and a parameter set that give you
>> good predictions, than you can plug in your two samples, and get your
>> results.
>>
>>
>>
>> In my opinion this kind of analysis is simpler in R than any of the
>> programs you mentioned below, since it requires frequent revising of the
>> GPA fit (scale or not scale), use allometric regression (or not), use all
>> derived coordinates (or not), etc… to get the best ‘tuned’ model for your
>> existing data.
>>
>>
>>
>> There are other ways than LD to do this, but the structure of the problem
>> is more or less the same: Derive a model, test it, if it results in
>> sensible predictions, then apply to your unknowns.
>>
>>
>>
>> Hope this helps some…
>>
>> M
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> *From:* Dr.Abdelnasser Ibrahim 
>> *Sent:* Monday, April 2, 2018 4:11 AM
>> *To:* morphmet@morphometrics.org
>> *Subject:* [MORPHMET] How can I test the project for identification of
>> unknown crania?
>>
>>
>>
>> Dear all,
>>
>>
>>
>> I'm a Ph.D. student and I'm working with 3D CT scan geometric
>> morphometric analysis for classification of known Malaysian crania to
>> different sexes and ancestries.
>>
>>
>>
>> I collected the landmarks using Stratovan software and did Principal
>> component, canonical variate, procrustes ANOVA, and DF analysis using
>> MorphoJ and SPSS. and did visualization using IDAV Landmark Editor software
>> and did cluster analysis using PAST software and used SPSS for reliability
>> tests.
>>
>>
>>
>> I need to classify unknown crania. How can I test the project for
>> identification of unknown crania?
>>
>> I did CT scan for known 2 crania one male and another female and known
>> ancestries. Then I collected the landmarks on the tested crania using the
>> same protocol. Then I added the collected coordinates to the whole
>> previously collected coordinates 

Re: [MORPHMET] Landmarks coming up funny in MorphoJ

[K. James Soda]

Dear Ms. Neves,

Quick addition to Dr. Rohlf's solution.  To change a file extension on a
Windows machine, you will need to do the following:

1) Open a Windows Explorer window.
2) On the ribbon at the top of the window, click "View"
3) Click the box for "File Name Extensions" (in the "Show/Hide" section)

You can now edit the file extension (i.e., change .txt to .tps).

I apologize if that was obvious or if you are not using a Windows machine;
that's hung me up in the past.

Hope this helped,

James

On Mon, Nov 6, 2017 at 4:46 PM, f.james.rohlf 
wrote:

> Let it save it as a txt file then just rename the file.
>
> -
> F. James Rohlf
> Depts. of Ecology & Evolution and of Anthropology
> Stony Brook University
>
>  Original message 
> From: Candice Neves <536...@students.wits.ac.za>
> Date: 11/6/17 12:59 PM (GMT-05:00)
> To: Guido Rocatti 
> Cc: MORPHMET 
> Subject: Re: [MORPHMET] Landmarks coming up funny in MorphoJ
>
> Hi Guido
>
> Thanks so much for the suggestion, I'll definitely do that. Is there a
> particular way to save a wordpad/notepad file as a .tps format file? I've
> tried to do this before but it kept saving as a .txt file, or would this
> still work?
>
> Thanks for the help
> Candice Neves
>
> On 6 November 2017 at 19:44, Guido Rocatti  wrote:
>
>> Hi Candice,
>>
>> I think that maybe you should try to organize both coordinate sets
>> directly using notepad or wordpad and saving the file as a .tps format
>> file. Doing so, you won't depend on the tpsUtil.
>> Also, I understand that you have missing data. You can try and estimate
>> those missing landmarks with the *estimate.missing *function of
>> the  geomorph package for R. It uses thin-plate spline to interpolate
>> landmarks, or a linear regression model to predict them (the method
>> election is up to you).
>> Once you managed to put both sets together and you obtained your missing
>> landmarks, you can export your new dataset and open it on MorphoJ.
>> Hope this helps somehow.
>>
>> Good Luck.
>>
>>
>> Guido Rocatti
>> Lic. en Antropología.
>> División Antropología, Facultad de Ciencias Naturales y Museo.
>> Universidad Nacional de La Plata.
>> CONICET
>>
>>
>>
>> 
>>  Libre
>> de virus. www.avg.com
>> 
>> <#m_-6646032331324499958_m_7371067766444365814_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
>>
>
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Re: [MORPHMET] Does a research sample need to be normally distributed (male/female ratio) for PCA?

[K. James Soda]

Dear Dr. Hadi,

I would just like to add one additional comment to your question about the
need for normal distributions in PCA.  Dr. Mitteroecker is (of course)
correct that PCA does not make any distributional assumptions, but you
mentioned in passing that confidence ellipses are being placed around the
points.  Confidence ellipses usually do assume that the data is
multivariate normal.  There are distribution-free methods for placing
confidence intervals, but my suspicion is that these intervals would not
usually have an elliptical appearance unless the data was in fact normally
distributed.

So to reiterate:  PCA, no distributional assumptions.  Confidence ellipses,
usually assume multivariate normal data.

Hope that comment is useful,

James

On Thu, May 25, 2017 at 4:58 AM, Helmi Hadi  wrote:

> Dear morphometricians,
>
> Does a sample need to be normally distributed when conducting PCA in
> geometric morphometrics? Sometimes due to research constraints there are no
> samples of the opposite sex. Someone was asking me this question, and I do
> not have the answer. When I look at the data distribution, there is quite
> an imbalance male/female population. However, the classifiers male/female
> and species are there and you can sort of tell which group belongs to
> where. My only fear is that the confidence ellipse for the males are being
> "gravitated" towards the females for one species as that species does not
> have any male specimens. Attached are the file which I have recreated the
> dataset based on memory.
>
> Is this kind of data acceptable or publishable?
>
> My own personal question is based on the GMM results given in MorphoJ. The
> PC1/PC2 axes does not intersect at the middle (which I have personally
> drawn the dotted line there). I don't mind this output, but does it matter
> to have the axes cut at the 0 value? The data data distribution does not
> change with the change of axes lines. I noticed some GMM papers have the
> axes at 0.
>
> Thanks all for the help,
>
> Helmi Hadi,
> School of Health Scienes,
> Universiti Sains Malaysia
>
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Re: [MORPHMET] Interpreting PCA results

[K. James Soda]

Dear David,

Great question!  I disagree with the statement that the samples' variance
in shape space is not biologically real or, perhaps more accurately, is
less real than the variance in any other space.  As far as I see it, the
basic strategy in any biostatistical study, be it GM or otherwise, is that
a researcher represents a real biological population as an abstract
statistical population.  They then use this abstract statistical population
as a proxy for the real one so that inferences in the statistical space
have implications for the real world.

For example, a PCA finds a direction in the statistical space in which the
statistical population tends to be spread out.  This is interesting to the
researcher because this direction has a correspondence to certain real
world variables.  As a result, the PCA tells the researcher in what ways
the real population tends to vary.  The key point, though, is that the
researcher transitioned from the statistical space to the real world.

Moving from shape space to the real world is no different in principle.  We
have a real population of specimens, whose shape are of interest to us, and
we represent them using vectors of shape variables.  The vectors are
abstractions; it is not as if we can hold a vector in our hands.  However,
this is irrelevant because they are just proxies, no less real than any
other quantitative representation.  What matters is if we can tie them back
to the real world.  This is why morphometricians implement a visualization
step.  In a PCA, the PCs describe how our proxies vary, and we visualize in
order to see how this variation appears in the real world.  It is
infeasible to visualize every point along this axis, so we instead
visualize a handful.  Since the core goal in PCA (at least in this context)
is to describe variance, we generally describe the locations where a
visualization occurs in units of standard deviations from the mean.  We
could use absolute distances along an axis, but this is probably more
arbitrary than standard deviation units.  The standard deviations come from
the data's distribution, whereas the absolute distance is really only
well-defined in the mathematical space.

To summarize: i) Nearly all quantitative analyses involve an abstraction to
a mathematical space.  ii) The description of points in a mathematical
space is useful to the researcher because the researcher is able to
translate the abstract mathematical space into a real world
interpretation.  iii) In GM, the shape variables are traditionally
translated into the real world via visualization.  Ergo, morphometricians
often interpret PCA results via visualizations along individual PCs.  To
aid in interpretation, this tends to occur in standard deviation units
because the standard deviation is more easily tied to the real world
relative to arbitrary selecting a unit of distance.

Perhaps some of these points are up for debate, but remember that
statistics is largely the study of VARIATION.  If the variation in shape
space did not have any biological significance, almost no analysis after
alignment would be possible.

Hope somewhere in this long commentary, you found something helpful,

James

On Tue, May 9, 2017 at 4:56 PM, dsbriss_dmd  wrote:

> Good afternoon all, I have a question about interpretation of PCs.  I have
> come across several articles in orthodontic literature having to do with
> morphometric analysis of sagittal cephalograms that discuss warping a
> Procrustes analysis along a principal component axis.  Essentially the
> authors discuss finding whatever principal components represent shape
> variance, then determining the standard deviation(s) of those PC's, and
> applying the standard deviations to the Procrustes shape to warp the
> average shape plus or minus.  So if you have an average normodivergent
> Procrustes shape, one warp perhaps in the negative direction might give you
> a brachycephalic shape, while the opposite would give you a dolichocephalic
> shape.  But I don't know where this idea comes from.  I have been involved
> with 8 or 9 morphometrics projects over the last few years and I have never
> been able to figure this out or the rationale for performing such an
> application with the PC results.
>
> As an example of what I am talking about here is a passage from the
> Journal of Clinical & Diagnostic research, doi:  10.7860/JCDR/
> 2015/8971.5458 
>
> "Here, the first 2 PCs are shown & the Average shape (middle) was warped
> by applying each PC by amount equal to 3 standard deviations in negative
> (left) and positive (right) direction {[Table/Fig-10
> ]: PC1
> with standard deviation, [Table/Fig-11
> ] PC 2
> with standard deviation}."
>
> I did not include the graphs from the article but if it would help to
> answer 

Re: [MORPHMET] Use of PIC (Phylogenetic Independent Contrast)

[K. James Soda]

Dear Ms. Llopis,

It is my understanding  that you are correct, Brownian evolution is a
generally considered an assumption in PIC (see Felsenstein, J. 1985.
Phylogenies and the comparative method.  American Naturalist 125: 1-15).
Its inclusion is important to Felsenstein's formulation because it allows
the procedure to yield statistically independent variables, which is an
assumption in nearly all statistical analyses.

Hope this helps,

James Soda

On Sat, Feb 20, 2016 at 8:57 AM, Cristina Llopis <
cristina.llopis.beleng...@gmail.com> wrote:

> Dear all,
>
> I'm studying evolutionary modularity.
> I'm performing a PIC analysis on MorphoJ. I would like to know if Brownian
> Model is assumed here.
>
> Thank you in advance.
>
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