On 05/11/2018 18:50, Diego Ardón wrote:
Captura de pantalla 2018-11-05 a la(s) 11.40.26.png
Thank you Mr. Fruciano. I had already made the DFA, but wasn't aware the
graphical output represented both groups (it certainly makes sense). I
have a couple of other questions regarding semi-landmarks. I probably
should start a new topic, but I'll first try out here:
So, I was adviced to use semi-landmarks, I placed them with MakeFan8,
saved the files as images and then used TpsDig to place all landmarks,
however I didn't make any distinctions between landmarks and
semi-landmarks. What unsettles me is (1) that I've recently comed across
the term "sliding semi-landmarks", which leads me to believe
semi-landmarks should behave in a particular way.
Well, it's a long topic, but the general idea is that, to account for
the uncertainty in placement of a semilandmark along a curve, this is
slid along the curve itself (or, more frequently, its approximation) so
that ideally only variation perpendicular to the curve (reflecting the
curvature) is retained.
In current practice, semilandmarks are slid. Various software can do
this, the most popular for 2D data being certainly tpsRelW by F.J. Rohlf.
A good, recent and accessible treatment of this topic is:
Gunz & Mitteroecker 2013. Semilandmarks: a method for quantifying curves
and surfaces. Hystrix
The second thing that
unsettles me is whether "more semi-landmarks" means a better analysis.
Not necessarily.
I
can understand that most people wouldn't use 65 landmarks+semilandmarks
because it's a painstaking job to digitize them, however, in my recent
reads I've comed across concepts like a "Variables to specimen ratio",
which one paper suggested specimens should be 5 times the number of
variables. I do have a a data set of nearly 400 specimens, but it does
come short if indeed I should have 65*2*5 specimens!
There are two issues: 1. whether statistical procedures are defined, 2.
whether one has enough power and/or how large is error in estimates.
The first issue is easy to deal with: certain statistical procedures
(for instance, the ones involving matrix inversion) are not defined if
there are many variables and relatively few cases. These procedures
simply "don't work". However, there are other alternative procedures
which do work (e.g., the ones based on distances) and/or workarounds
(e.g., use of generalized inverses).
The second issue is much more complex and I doubt one can give a
straightforward answer. In general, the more observations (specimens)
the better (when one can get them, that is). But the idea of a certain
number of observations relative to the number of variables is, at best,
a rule of thumb.
Clearly, having too many variables can create problems and artifacts. An
interesting recent example of this can be found in
Bookstein 2016 - A newly noticed formula enforces fundamental limits of
geometric morphometric analyses. Evolutionary Biology
In your particular case, if I were you I would ask myself whether all
those points/semilandmarks are that necessary to capture biologically
relevant variation. That is a question that only you can answer, based
on your knowledge of the biological problem at hand.
Statistical power and reliability of estimates is another issue, which
is in part dataset-dependent (as well as dependent on which statistical
procedures you intend to use). An interesting paper dealing with this is
Cardini 2007. Sample size and sampling error in geometric morphometric
studies of size and shape. Zoomorphology
In general, as said above, it's very hard to give straightforward
answers to your question.
I hope this still helps, though.
Carmelo
==================
Carmelo Fruciano
Institute of Biology
Ecole Normale Superieure - Paris
CNRS
http://www.fruciano.it/research/
El lunes, 5 de noviembre de 2018, 2:12:20 (UTC-6), Carmelo Fruciano
escribió:
On 03/11/2018 22:28, Diego Ardón wrote:
> Dear Mr. Soda,
>
> Thank you for replying. Your statement " 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. " pretty much describes what
I have
> in mind to do. Which software could I use to do this? since I
believe
> MorphoJ will not do it.
Dear Diego,
MorphoJ will actually do it. The easiest is to use what is under the
menu "Discriminant analysis". MorphoJ's user guide has a brief but very
clear description of the graphical output.
I hope this helps.
Best,
Carmelo
--
==================
Carmelo Fruciano
Institute of Biology
Ecole Normale Superieure - Paris
CNRS
http://www.fruciano.it/research/ <http://www.fruciano.it/research/>
> El miércoles, 31 de octubre de 2018, 13:51:07 (UTC-6), K. James Soda
> escribió:
>
> 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 <diegoar...@gmail.com
> <javascript:>> 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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==================
Carmelo Fruciano
Institute of Biology
Ecole Normale Superieure - Paris
CNRS
http://www.fruciano.it/research/ <http://www.fruciano.it/research/>
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