[MORPHMET] geomorph 3.1.2 available

[Adams, Dean [EEOBS]]

Colleagues,

Version 3.1.2 of geomorph is now available on CRAN as source-file, and over the 
next few days the CRAN folks will generate the binaries also.

This version is a patch, fixing some issues created when the RGL library 
recently made changes to their package - regarding how colors/materials of 3D 
meshes were handled -  which caused some cascading issues for some using 
geomorph for 3D digitizing from surface scans. Additionally, a few other minor 
issues were also cleaned up, and are documented in the NEWS file. There are 
also two more vignettes to guide users through the geomorph experience.

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

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RE: [MORPHMET] How to fix erroneous 3D coordinates took by microscribe

[Adams, Dean [EEOBS]]

To add briefly to Dennis' comment. If landmark estimation is the route taken, 
both TPS-based and regression-based imputation are implemented in geomorph.

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

-Original Message-
From: morphmet  
Sent: Tuesday, May 7, 2019 8:18 AM
To: morphmet@morphometrics.org
Subject: Re: [MORPHMET] How to fix erroneous 3D coordinates took by microscribe

It depends on what is wrong with the data (I haven't checked it). 
Morpheus et al. can be used to correct data after visual inspection or mark bad 
data points as missing or delete whole sets of coordinates for particular 
points.

The easiest way to do this is to get the data into a form importable by 
Morpheus - NTSYSpc is the easiest, and import the data. You can then set up 
links between points (there is a menu option). Then, you can just page through 
the objects looking for one whose links are obviously wrong. You can then swap 
points (another menu option) until they are in the correct order. For this, it 
is useful to use the plot Opts button to set the point plotting symbol as 
"number", which locates the points with their number, e.g., "1", "2", etc.

You can also mark any offending points (with genuinely bad data) as "missing" 
or delete points/landmarks. If there are too few or too many, you can insert 
missing points (which will be marked as missing data) or delete specific points 
from specific objects.

Another suggestion to find objects with bad points is to do a PCA plot (I use 
R) to check for any outliers.

If there are a few scattered points with missing data at the end of all of 
this, the data can be filled in using missing data imputation - mean 
substitution is the only method currently implemented. Mean substitution 
generally does not affect parameter estimates, but does result in error 
estimates that are undervalued - there is more variance than apparent in the 
data since mean values were substituted for missing data.

-ds

PS: NTSYSpc format for import. On the first line, 1 is NTSYS for rectangular 
data matrix, n is for the number of objects in the file (you have to provide a 
number, not 'n'); a number representing the number of landmarks, p, times the 
number of dimensions, 3 in your case; and a '0' 
indicating there are no missing data in the data set. If there are missing 
data, then the '0' is replace by something like "1 -999", where the '1' 
indicates the presence of missing data and the next item, e.g., -999, is the 
code used to indicate missing data...

1 n nDimxp 0
x1 y1 z1
x2 y2 z2
...
xp yp zp

...repeated (except for the first line) for more objects

On 5/7/19 8:09 AM, Azadeh Mohaseb wrote:
> Dear all
> I am a post-doc researcher on GMM and I work on equids bones. Recently, I 
> digitized some modern equid bones by a microscribe and then I realized that 
> the 3d coordinates of some of these individuals are not correcte.
> As I don't have access to these bones to digitize them again, I wondered if 
> you could help me to fix this problem.
> I send you the correct coordinates of one individual and the incorrect 
> coordinates of another one. These two individuals have been digitized at the 
> same time, with same microscribe and the same settings on machine and 
> software.
> 
> Individual 1: correct
> 
> 228.6438  203.1991156.5325
> 220.2511  204.6701143.3459
> 223.9106  202.3835121.6829
> 242.5923  225.1355121.4050
> 240.4551  225.8690136.0673
> 238.9128  226.4731141.2689
> 240.9662  226.0292146.4538
> 243.8571  223.4773158.7283
> 248.3989  203.3913162.7939
> 247.9284  204.2474147.2226
> 249.7594  202.0327140.3145
> 247.2640  205.0845134.1143
> 246.3540  204.5467115.9491
> 88.1469   334.5199162.2996
> 79.0490   330.4259152.7271
> 74.0790   325.4809142.4243
> 79.8139   324.6076119.4693
> 86.4637   331.0746115.0139
> 88.8453   333.9695120.4097
> 87.0741   333.9286128.0646
> 85.8100   336.1603144.5840
> 83.5972   335.3779150.5125
> 90.8324   337.0566156.8697
> 92.3339   337.9303154.
> 87.5753   339.9321145.9987
> 93.6211   346.3407145.2005
> 98.7993   344.1494152.6361
> 89.2627   341.3373127.1407
> 91.0691   339.7504123.6672
> 94.9706   342.5350123.9575
> 93.9787   344.1297127.5435
> 
> Individual 2: incorrect
> 
> 117.1907  72.8549 331.5414
> 63.8556   106.7571

[MORPHMET] geomorph 3.1.1 released on CRAN

[Adams, Dean [EEOBS]]

Colleagues,

I'm pleased to announce that geomorph 3.1.1 is now available on CRAN. As 
always, all changes are noted in the NEWS file and in the appropriate help 
pages. To install geomorph 3.1.1 from within R, simply type:

install.packages('geomorph')

We wish to emphasize that this was a rather substantial update; thus, users are 
highly encouraged to consult all help files for additional information. Though 
some functions have been deprecated, all analyses previously available in 
geomorph are still available (though some more advanced options are now 
accessed in slightly modified form).  Also, to ease users through this 
transition, we have developed a vignette that describes the differences in the 
current version, and how to implement various analyses using the newer 
notation. The vignette is distributed with the installation of geomorph, and 
may be accessed by typing:

browseVignettes(package="geomorph")

In particular, users will notice a change in usage of the 'procD' family of 
functions for assessing linear models. Beginning with geomorph 3.1.1, only 
'procD.lm' and 'procD.pgls' remain, and these have greatly enhanced 
functionality. For instance, one may now utilize type I, type II, or type III 
sums of squares, may specify the error term against which various factors are 
evaluated (to incorporate random effects in simple mixed model designs), and 
may incorporate object-covariance matrices describing non-independence.  
Additionally, pairwise comparisons of means, of slopes, and of within-group 
variation may be accomplished using the 'pairwise' function based off of fitted 
models from 'procD.lm' or 'procD.pgls'. The functions: procD.allometry, 
advanced.procD.lm, and nested.update, have all been deprecated, as their 
analyses are now accomplished (and enhanced) via these other functions. These 
enhancements were made possible by use of the RRPP package (Collyer and Adams, 
2018: Methods Ecol. Evol) which is now more tightly interconnected with 
geomorph.

Finally, for graphical visualization of shapes, we encourage users to explore 
the new picknplot.shapes function, which allows one to select locations from 
just about any kind of plot obtained in geomorph, and generate estimates of 
shapes that correspond to those locations (much like in the TPS series). Also, 
the new plotAllometry function provides several graphical plots commonly used 
in allometric studies. We hope these new graphical options provide additional 
flexibility and usability of geomorph.

Best to all,

Dean, Mike, and Antigoni (the geomorph team)


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

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RE: [MORPHMET] Visualizing shape differences in PCA

[Adams, Dean [EEOBS]]

Charles, 

There are several ways to do what you wish within geomorph.  First, the 
plotTangentSpace function does allow one to specify which axes are plotted. 
This can be useful for obtaining shape deformations along the extremes of axes 
other than PC1 and PC2. 

A more flexible option is to use the function shape.predictor. Here, one 
provides scores, say for a given PC or a regression, and can obtain predicted 
shapes based on them. The help file provides example usage for this, and many 
other  applications (e.g., obtain shape deformations for mean shapes, predicted 
shapes, etc.).

Finally, I will also mention that we are about to release a new version of 
geomorph which will include capabilities to select locations in statistical 
plots, which will allow shape visualization of those locations in real time.  I 
will post more on this in the near future.

Hope this helps,

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


-Original Message-
From: Charles Zhang  
Sent: Monday, March 25, 2019 4:38 PM
To: MORPHMET 
Subject: [MORPHMET] Visualizing shape differences in PCA

Dear all,

I'm using the Geomorph package in R to do the PCA. It shows the negative-most 
and positive-most shapes of a PC, which is really great. I just want to ask if 
there is any way to visualize shape change along a certain PC? 

I know that Morphologika does this by allowing one to move the cursor along a 
PC to visualize shape change. However, when I import my data to process in the 
Morphologika, it always reports error. Perhaps it is because I use too many 
semilandmarks but too few specimens.

Could you please tell me if there is an alternative way to visualize shape 
differences along a PC, rather than just at the two-end points?

Thank you very much for taking your time to look at my question!

Best regards,
Charles

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RE: [MORPHMET] Re: semilandmarks in biology

[Adams, Dean [EEOBS]]

Folks,
 
I think it is important to recognize that the example in Andrea’s earlier post 
does not really address the validity of sliding semilandmark methods, because 
all of the data were simulated using isotropic error. Thus, the points called 
semilandmarks in that example were actually independent of one another at the 
outset.
 
Yet a major reason for using semilandmark approaches is the fact that points 
along curves and surfaces covary precisely because they are describing those 
structures. Thus, this interdependence must be accounted for before shapes are 
compared between objects. The original literature on semilandmark methods makes 
this, and related issues quite clear.
 
What that means is that evaluating semilandmark methods requires simulations 
where the points on curves are simulated with known input covariance based on 
the curve itself (difficult, but not impossible to do). But using independent 
error will not accomplish this.
 
The result is that treating fixed landmarks as semilandmarks can lead to what 
some feel are unintended outcomes, just as treating semilandmarks as fixed 
points are known to do (illustrated nicely in Figs 1-4 of Gunz et al. 2005). 
But both are mis-applications of methods, not indictments of them. 

As to the other points in the thread (the number of semilandmark points, etc.), 
earlier posts by Jim, Philipp, and Mike have addressed these.
 
Dean

Dr. Dean C. Adams
Director of Graduate Education, EEB Program
Professor
Department of Ecology, Evolution, and Organismal Biology
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

-Original Message-
From: andrea cardini  
Sent: Wednesday, November 7, 2018 4:31 AM
To: morphmet@morphometrics.org
Subject: Re: [MORPHMET] Re: semilandmarks in biology

Making cool pictures has a purpose only if both the pics and the numbers behind 
them are accurate. It's not an aim in itself, I hope (although this is the 
second time I hear that one should add as many points as needed to see a nice 
picture). Parsimonious explanations are, to me, much more appealing than nice 
pictures (as much as I like a beautiful visualization), but that might be a 
matter of taste.

Philipp, could you clarify what "homology function" means?
We're not saying that sliding creates homology, as I sometimes read in papers, 
are we?

No doubt one does not expect anatomical regions of an organism to be 
independent. The open question to me is what the biological covariance is and 
what is the bit added by superimposing and maybe sliding. I suspect that on 
this there's no universal answer: it will be dependent on the study organism, 
the number and distribution (and type) of landmarks etc. In some studies it 
might not matter much, but in others may be much more relevant.

Thanks all for the comments.
Cheers

Andrea

On 06/11/2018 20:53, mitte...@univie.ac.at wrote:
> Yes, it was always well known that sliding adds covariance but this is 
> irrelevant for most studies, especially for group mean comparisons and 
> shape regressions: the kind of studies for which GMM is most 
> efficient, as Jim noted.
> If you consider the change of variance-covariance structure due to (a 
> small amount of) sliding as an approximately linear transformation, 
> then the sliding is also largely irrelevant for CVA, relative PCA, 
> Mahalanobis distance and the resulting group classifications, as they 
> are all based on the relative eigenvalues of two covariance matrices 
> and thus unaffected by linear transformations. In other words, in the 
> lack of a reasonable biological null model, the interpretation of a 
> single covariance structure is very difficult, but the way in which 
> one covariance structure deviates from another can be interpreted much easier.
> 
> Concerning your example: The point is that there is no useful model of 
> "totally random data" (but see Bookstein 2015 Evol Biol). Complete 
> statistical independence of shape coordinates is geometrically 
> impossible and biologically absurd. Under which biological (null) 
> model can two parts of a body, especially two traits on a single 
> skeletal element such as the cranium, be complete uncorrelated?
> 
> Clearly, semilandmarks are not always necessary, but making "cool 
> pictures" can be quite important in its own right for making good 
> biology, especially in exploratory settings. Isn't the visualization 
> one of the primary strengths of geometric morphometrics?
> 
> It is perhaps also worth noting that one can avoid a good deal of the 
> additional covariance resulting from sliding. Sliding via minimizing 
> bending energy introduces covariance in the position of the 
> semilandmarks _along_ the curve/surface. In some of his analyses, Fred 
> Bookstein just included the coordinate perpendicular to the 
> curve/surface for the semilandmarks, thus discarding a large part of 
> the covariance. Note also that sliding via minimizing Procrustes 
> distance introduces only 

RE: [MORPHMET] Re: Conceptual clarification of plotting shape deformation grids in geomorph

[Adams, Dean [EEOBS]]

Igor,

The components, $means contain the least squares means from the linear model 
implemented in trajectory analysis. These can be visualized relative to some 
reference (e.g., the overall mean shape), using ‘plotRefToTarget’.  Note that 
the $means must first be converted to a 2D array using ‘arrayspecs.’

An example is below (see also relevant help files).

Best,

Dean

##
data(plethodon)
Y.gpa <- gpagen(plethodon$land)
gdf <- geomorph.data.frame(Y.gpa, species = plethodon$species, site = 
plethodon$site)
TA <- trajectory.analysis(coords ~ species*site, data=gdf, iter=199)

new <- arrayspecs(TA$means,p=12,k=2)
ref <-mshape(Y.gpa$coords)
plotRefToTarget(ref,new[,,1])
##

Dr. Dean C. Adams
Director of Graduate Education, EEB Program
Professor
Department of Ecology, Evolution, and Organismal Biology
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Igor Talijančić 
Sent: Monday, November 5, 2018 4:26 AM
To: MORPHMET 
Cc: mlcoll...@gmail.com
Subject: [MORPHMET] Re: Conceptual clarification of plotting shape deformation 
grids in geomorph


Hello everyone,

Just a question regarding the plotting of deformation grinds of the trajectory 
analysis (e.g. pupfish or plethodon data). Can shape.predictor function be used 
for visualizing TA$pc.means since TA$pc.data corresponds to PC scores obtained 
for Y.gpa$coords?



Thank you for your given time and consideration.



Sincerely,

Igor

Dana srijeda, 25. srpnja 2018. u 14:42:41 UTC+2, korisnik javiersantos3 napisao 
je:

Hello Carmelo and Mike,



Thanks for the quick response! I see things now clearer, especially with the 
examples you have both provided. Sometimes one gets disoriented in the 
abstractness of shape space and coding ;-P  Thanks again!





Best wishes,

Javier






From: Mike Collyer >
Sent: Wednesday, July 25, 2018 2:29:38 PM
To: Javier Santos
Cc: Morphomet Mailing List
Subject: Re: Conceptual clarification of plotting shape deformation grids in 
geomorph

Javier,

First your plotting question.  The plot.trajectory.analysis function is an S3 
generic plot function, which means you can modify the plot as you like.  You do 
this easiest with the points function.  Here is an example, using the help page 
example, which hopefully makes sense for you:


data(plethodon)

Y.gpa <- gpagen(plethodon$land)

gdf <- geomorph.data.frame(Y.gpa, species = plethodon$species, site = 
plethodon$site)



TA <- trajectory.analysis(coords ~ species*site, data=gdf)

summary(TA, angle.type = "deg")

plot(TA)

# Augment plot with the following code

points(TA$pc.data, pch=19, col = "blue”) # turn all points blue

points(TA$pc.data, pch=19, col = TA$groups) # change points to different 
colors, by group
One can modify plots as desired but you might need to learn how to use 
graphical parameters in order to do it.  See the help for the function, par, to 
know how to do that.

Second, since PC scores are Procrustes residuals (coordinates) projected onto 
PC axes, there is a direct correspondence between an observation’s set of 
coordinates and its PC scores.  If you perform trajectory analysis, the $means 
object has the coordinates for the means (trajectory points).  You simply have 
to rearrange the values with arrayspecs to generate deformation grids.  The 
$pc.data is a matrix of PC scores whose rows correspond to the coordinates in 
the gpagen object.  For example, TA$pc.data[5,] is a set of PC scores for 
Y.gpa$coords[,,5].

Finally, for your last question, the function shape.predictor does exactly what 
you seek.  The help page has examples that should help you (on e specifically 
for allometry).

Cheers!
Mike

On Jul 25, 2018, at 7:17 AM, Javier Santos > 
wrote:

Hello Morphometricians,

I was hoping someone could clarify the concept of plotting shape deformation 
grids from the geomorph output. I am confused at the moment because the output 
of most functions (eg. trajectory.analysis()) gives PC values or regression 
scores, while most of the plotting functions I know (eg. plotRefToTarget(), 
plotTangentSpace(), plotAllSpecimens()) require LM coordinates. I am sure that 
the conceptual framework to plot the shape deformation grids corresponding from 
the PC/regression values of the functions' output should not be too 
complicated, but I am currently lost how to do so with the coding and do not 
have a working example.

I will use my current analysis as an example from which to work upon. I have 
ran a trajectory.analysis() on a three species sample:

ontogeny <- trajectory.analysis(M2d ~ 
species*age,f2=NULL,iter=999,seed=NULL,data=gdf)

and plot the results:

x11(); 
plot(ontogeny,group.cols=c("red","blue","green"),pt.scale=1.5,pt.seq.pattern=c("black","gray","white"))

The following code plots the trajectory in the corresponding PC1-PC2 
morphospace with each species' trajectory in a different color, however, 
although the lines are different colors, 

[MORPHMET] geomorph 3.0.6 released

[Adams, Dean [EEOBS]]

Morphometrics colleagues,

Version 3.0.6 of geomorph has now been released and is up on the CRAN 
repository for download and installation. As with all version updates, changes 
have been documented in the News file.

Of particular note is the incorporation of a refined residual randomization 
permutation procedure (RRPP) which is more general than its predecessor. For 
OLS models, the method is equivalent to the previous version and thus 
unchanged. However, for GLS models, the refined RRPP procedure is preferable 
and more widely applicable for statistical designs such as phylogenetic 
regression, phylogenetic anova, and phylogenetic factorial models.

A discussion of the procedure, and of the theory of exchangeable units from 
which the method is derived, is found in a recent paper in Evolution: Adams and 
Collyer. 2018. Phylogenetic anova: group-clade aggregation, biological 
challenges, and a refined permutation procedure. (found at  
https://onlinelibrary.wiley.com/doi/abs/10./evo.13492 ). Also, for the 
macroevolutionary biologists in the group, that paper also discusses how group 
dispersion across the phylogeny can affect statistical and thus biological 
inferences with respect to covariation patterns with phenotypic traits.

Best,

Dean (on behalf of the geomorph team: Dean, Mike, and Antigoni)

Dr. Dean C. Adams
Director of Graduate Education, EEB Program
Professor
Department of Ecology, Evolution, and Organismal Biology
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] Multivariate PICs?

[Adams, Dean [EEOBS]]

Sarah,

As far as I am aware, MorphoJ estimates a set of PICs one dimension at a time. 
Thus, for a multivariate dataset of N species and p-trait dimensions, this will 
return a matrix of N-1 by p.  Here, each of the N-1 rows (nodes of the 
bifurcating tree) contains a vector of PICs, one per trait dimension.  This 
approach is simple to replicate in R: just use the pic function and loop across 
trait dimensions. 

Note that this differs from McPeek's formulation of multivariate independent 
contrasts (see e.g., McPeek et al. 2008: Am. Nat.). For this approach, 
differences in each of the p-dimensions of data are combined in the numerator 
of his equation, resulting in a a single PIC for each of the N-1 nodes. As you 
mentioned, these are equivalent to the Euclidean distance between taxa 
standardized by branch lengths. 

While the math of McPeek's procedure is simple enough, I am unaware of a 
function in R that estimates these directly. I also do not believe these are 
found in MorphoJ, but someone who uses that program could better respond to 
that query.

Hope this helps.

Dean

Dr. Dean C. Adams
Director of Graduate Education, EEB Program
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834


-Original Message-
From: Sarah Friedman  
Sent: Monday, March 19, 2018 2:24 PM
To: MORPHMET 
Subject: [MORPHMET] Multivariate PICs?

Hi all,

Primarily, I'm wondering if there is a way to calculate phylogenetic 
independent contrasts on multivariate shape data in R. However if no R method 
exists, I know a method to do this is implemented in MorphoJ, and I could 
possibly code an analogous method in R but I'm unsure how the contrasts are 
calculated. Does anybody know if the independent contrasts are calculated using 
the method described in McPeek (1994), the Euclidean distances between 
contrasts on each branch, or another method? 

Thanks,
Sarah

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RE: [MORPHMET] relative positions of landmark partitions in integration tests - how important?

[Adams, Dean [EEOBS]]

Christy,

That data example contained variation in relative jaw position among specimens, 
which could affect shape estimates, as well as down-stream shape analyses.  
Several approaches have been proposed for dealing with such rotational 
variation (see Adams 1999; also Bookstein’s Orange book). One approach is to 
rotate one subset of landmarks so the angle between subsets is invariant across 
specimens. The geomorph function ‘fixed.angle’ performs this operation for 2D 
landmark datasets.

As to your comment on whether or not such positional variation makes a 
difference, yes it can. PLS examines the degree of covariation between blocks 
of variables and estimates of the between block covariation will differ if one 
set of variables is rotated relative to the other. Whether this results in a 
large or small difference in r-PLS values is data-dependent, but the values 
will not be the same.

For this reason, prior to any PLS analysis for evaluating integration and 
covariation patterns, one must first carefully consider what type of variables 
are being utilized, how they were generated, and whether it even makes sense to 
interpret the results biologically.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Christy Anna Hipsley [mailto:chips...@unimelb.edu.au]
Sent: Tuesday, January 23, 2018 9:34 PM
To: MORPHMET 
Subject: [MORPHMET] relative positions of landmark partitions in integration 
tests - how important?

Hi all,

I'm trying to run an integration test in geomorph, or rather phylo.integration 
on 2 sets of Procrustes aligned coordinates for cranium and jaws of lizards, 
landmarked on both sides. When I plot the results I get graphs of the landmarks 
for the positive and negative extremes of PLS1&2, but for the cranium they are 
in lateral view while for the mandibles they are in frontal view.
I'm wondering if this is an issue for the estimation of r-PLS, since in Adams & 
Felice 2004 (PLoS ONE: Assessing Trait Covariation and Morphological 
Integration on Phylogenies Using Evolutionary Covariance Matrices), they write 
"the position of the jaw was standardized relative to the skull by rotating the 
jaw to a common articulation angle among specimens".

If it is an issue, how do I rotate the position of one of the partitions to be 
in the same orientation as the other?

Thanks for any advice!
Christy

Dr Christy Anna Hipsley | ARC DECRA Fellow
School of BioSciences, University of Melbourne/
Museums Victoria
GPO Box 666
Melbourne, Victoria 3001 Australia
T: +61 3 8341 7423 E: 
christy.hips...@unimelb.edu.au; 
chips...@museum.vic.gov.au
http://blogs.unimelb.edu.au/hipsleylab/
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RE: [MORPHMET] Doubt about scalling photos

[Adams, Dean [EEOBS]]

Anderson,

I don’t think you appreciated the importance of the Murat’s comments on your 
earlier post on this same topic.

In theory, there is no problem combining objects digitized at different 
magnifications, or even digitized by different researchers. However, before 
doing so one must carefully investigate for possible systematic biases in 
digitizing, so they may be reduced to the greatest extent possible. If there is 
some consistent bias in how objects are digitized in one ‘group’ relative to 
the other, this will permeate into perceived differences in shape that may not 
exist. A common example with older digitizing tablets would be differences in 
digitizing due to the handedness of the person digitizing. Right-handed and 
left-handed individuals hold the stylus differently which can result in 
consistent perceived shape differences of the same objects once digitized.

Whether or not you have such an issue with your two magnifications is unclear. 
However, it is impossible to evaluate this without additional replication. 
Again, as Murat suggested, try digitizing each object multiple times at each 
magnification. Then one could obtain estimates of the variation in digitizing 
at the same magnification versus across magnifications to begin to discern 
whether the between-magnification variation is greater than one might expect. 
If it is, then one must dig deeper to determine why.

I would recommend sorting all of this out before embarking on your empirical 
study. Otherwise, interpreting patterns in the final dataset becomes 
challenging to say the least.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Anderson Feijo [mailto:andefe...@gmail.com]
Sent: Friday, December 29, 2017 3:19 AM
To: MORPHMET 
Subject: [MORPHMET] Doubt about scalling photos

Hi everyone,

I am starting a new project using GM which I will work with groups with 
different sizes (e.g., rodents and small carnivores). I would like to find a 
way to use the whole dataset in the analyses, instead of perform set of 
analyses for each sized group. So, I did a test using one skull and place the 
camera in two different distances to the object (~15 cm and ~30 cm). My 
expectation was after scaling (using tpsDig) I wouldn´t have any meaningful 
difference. But I got two clear groups that were statistically different. So, 
my question is how can I combine 2D landmarks based on photos taken from 
different distances of the camera to the object. I have attached here the tps 
file (10 copies of the same skull, five at ~15cm and five at ~30cm). I would be 
very grateful for any suggestion.

All the best and Happy 2018!

Anderson
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RE: [MORPHMET] Issues with subsetting nts array with classifiers and plotTangentSpace visualization

[Adams, Dean [EEOBS]]

Skip,

It is difficult to troubleshoot this without seeing your full code and data 
file. If you send me those off-line I can try to take a look in the near future 
to identify what is going on.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Phillip Skipwith [mailto:pskipw...@gmail.com]
Sent: Friday, January 5, 2018 4:00 PM
To: MORPHMET 
Subject: [MORPHMET] Issues with subsetting nts array with classifiers and 
plotTangentSpace visualization

Hi,

I'm using Geomorph to analyze a 3D landmark dataset. I'm having a number of 
issues both in subsetting my array and visualizing these data after Procrustes 
analysis. I'm working with a set of 28 specimens and 61 3D landmarks. Below is 
a sample of my .dta file from Landmark Editor.


trial.coords <- readland.nts('pseudoxy_project3.dta')

1 28L 183 0 Dim=3

C_infralineatus_AMNH165799
D_quadralineatus_AMNH153343
D_quadralineatus_AMNH153345
D_quadralineatus_AMNH160061
..
-9.8451595e+000  -2.8541670e+000  -2.8408973e+000
-1.0106355e+001  -5.0837624e-001  -1.3406076e+000
-8.7275896e+000  -2.4869413e+000  -1.2266474e+000
-7.3323584e+000  -3.362e+000  -3.0307722e+000
..

I then make classifiers for later grouping and subsetting based on species 
(please see below). I can neither subset the data or properly visualize the 
resulting PCA plot. I get can error regarding the class of my array dimnames.  
Instead of a PCA plot of the designated axes organized by species, I get two 3D 
plots of PC1 (positive and negative loadings) with no labeling or legend. Below 
is some of my simple code. I'm new to Geomorph, so any help or advice would be 
greatly appreciated. If need be, I can send a reduced dataset to help with 
troubleshooting.


pseudoxy.gpa <- gpagen(trial.coords, ProcD = TRUE, Proj = TRUE, print.progress 
= TRUE)

# PCA
categories.qpa <- strsplit(dimnames(pseudoxy.gpa$coords)[[3]], "_")
# unlist into matrix format
classifiers.qpa <- matrix(unlist(categories.qpa), ncol=3, byrow=T)
# add the specimen ID to the first column of the table
classifiers.qpa <- cbind(dimnames(pseudoxy.gpa$coords)[[3]], classifiers.qpa)
# rename the column headings
colnames(classifiers.qpa) <- c("FullID", "Genus", "Species", "ID")
# converts to data frame so can index using $
classifiers.qpa <- as.data.frame(classifiers.qpa)

gp <- as.factor(paste(classifiers.qpa$Genus, classifiers.qpa$Species)) # create 
grouping variable
sub.gpa <- coords.subset(A = pseudoxy.gpa$coords, group = gp)
# I get the following error
Error in dimnames(specimens)[[3]] <- names : 'dimnames' must be a list #trying 
to coerce dimnames to a list does not help

pseudoxy.PCA <- plotTangentSpace(pseudoxy.gpa$coords, groups = gp, label = 
TRUE, legend = TRUE, axis1 = 1, axis2 = 2, warpgrids = TRUE)

# See attachment for confusing plot.

Thanks in advance,

Skip


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RE: [MORPHMET] Procrustes fit

[Adams, Dean [EEOBS]]

Andrey,

It is unreasonable to expect the numbers will match perfectly between these two 
software packages, as the way in which they perform the operations differs.  
First, MorphoJ uses Full Procrustes fit, whereas the TPS series, geomorph, and 
others use Partial Procrustes fitting. That will make a difference.

Second, there may be additional differences in in how the superimponsed 
specimens, and thus the consensus, is aligned relative to the X-Y coordinate 
system. Some packages allow one to rotate the consensus and aligned specimens 
to their principal axes post-superimposition. That too could lead to 
differences.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Andrey Lissovsky [mailto:andlis...@gmail.com]
Sent: Tuesday, October 31, 2017 9:26 AM
To: MORPHMET 
Cc: andlis...@gmail.com; volk...@yandex.ru
Subject: Re: [MORPHMET] Procrustes fit

Thank you, Andrea

I understand that difference should be tiny, so something goes wrong. I enclose 
one of my tps files. Usually I check dots and commas, so the reason is probably 
in some different way..
It is possible that I am mixing up menu items.. Last time I use this software, 
the labels were different.
Now I use:
In MorphoJ: Preliminaries -- New Procrustes fit -- Align by principle axes
then: Export dataset -- Procrustes coordinates
In TPS Relw: Actions -- Consensus
then: File -- Save -- Aligned specimens
Is this ok? Should these chains lead to the same results?

On Tuesday, October 31, 2017 at 5:04:56 PM UTC+3, alcardini wrote:
Andrey, the last time I checked this (last July, I believe), differences
between MorphoJ and TPSRelw were tiny and negligible. I compared MorphoJ
with R in the last days, and again differences were tiny.

The first thing I'd check is whether there's an issue with commas vs
dots as decimal separators.
If you send me the tps file, I can give a quick look.

Cheers

Andrea
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RE: [MORPHMET] reading shapes with only semilandmarks, Error in readland.tps: No landmark data for some specimens.

[Adams, Dean [EEOBS]]

Gonzalo,

You received that error message because you have no primary landmark points 
(LM=0 in your example). All of your points are in the form of curve points 
representing semilandmarks.

One solution is as you suggested: simply remove the curve lines so the points 
are read in as landmarks.  Since semilandmarks are identified later in the gpa 
step by using a curve designator matrix, this change in how you read in the 
data will not matter (so long as you remember to create and use the curve 
designators!). 

In future versions of geomorph we can investigate adding a catch for situations 
such as yours where all landmarks in the TPS file are in the form of 
semilandmarks in curve matrices.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834


-Original Message-
From: Gonzalo Bilbao [mailto:gonzalobil...@ucm.es] 
Sent: Tuesday, September 19, 2017 1:29 PM
To: MORPHMET 
Cc: gonzobil...@hotmail.com
Subject: [MORPHMET] reading shapes with only semilandmarks, Error in 
readland.tps: No landmark data for some specimens.

Hi,

I am trying to analize a dataset ("myshapes.tps") with some 1000 shapes that 
look like this:

LM=0
CURVES=2
POINTS=8
1467.0 1155.0
1447.0 1169.0
1432.0 1189.0
1418.0 1208.0
1403.0 1228.0
1393.0 1250.0
1383.0 1273.0
1376.0 1296.0
POINTS=8
1403.0 1617.0
1411.0 1596.0
1419.0 1575.0
1425.0 1553.0
1433.0 1532.0
1439.0 1510.0
1441.0 1488.0
1443.0 1466.0
IMAGE=E_suberosa_6_copy_std.jpg
SCALE=0.008129


So only semilandmarks, in two curves. When I import them in geomorph using 
readland.tps("myshapes.tps", specID = "imageID", readcurves = T), I get the 
following error:

Error in readland.tps("myshapes.tps", specID = "imageID", readcurves = T) : 
  No landmark data for some specimens.

Why do I get this error message? Is this command suppossed to use only 
landmarks (not semilandmarks)? 

Alternatively, I was thinking of removing the curves so all my points would be 
considered as landmarks, and defining sliders for the gpa alignment...

Thanks in advance for your replies!
Gonzalo

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[MORPHMET] geomorph 3.0.5

[Adams, Dean [EEOBS]]

Fellow morphometricians,

I have two announcements regarding geomorph.  First, a new version of geomorph 
(version 3.0.5) is available on CRAN, as well as on the geomorph github 
repository (https://github.com/geomorphR/geomorph). As always the list of major 
changes is found in the NEWS file that comes with the package.

Second, when citing geomorph, please be sure to cite that version used in the 
study. Many new functions and options are available in the current release as 
compared to the original version, so an accurate citation of the package 
ensures that others may replicate the procedures used in your study.  For 
example, the proper citation for the current version is:

Adams, D. C., M. L. Collyer, A. Kaliontzopoulou, and E. Sherratt. 2017. 
Geomorph: Software for geometric morphometric analyses. R package  version 
3.0.5. https://cran.r-project.org/package=geomorph.

Best,

Dean, Mike, Emma, and Antigoni (the Geomorph  Team)


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] FW: geomorph 3.0.4 now available

[Adams, Dean [EEOBS]]

Hello all,

Just a quick announcement to let you know that geomorph version 3.0.4 is now 
available on CRAN. The list of new features is below, and includes:

o An option to use types I, II, or III sums of squares and cross-products for 
ANOVA analyses (e.g., procD.lm, procD.pgls)
o PGLS option for advanced.procD.lm
o An option to choose between SS, F-values, or Cohen's f-squared to 
calculate effect sizes for ANOVA analyses (where applicable: see help files).
o Effect sizes are now centered on mean values from distributions of random 
statistics.  Statistics are also log-transformed in certain cases to normalize 
distributions.
o An option to use only an intercept as a model for procD.lm and 
procD.pgls, with limited output.
o An option to use permutation tests when comparing net evolutionary rates 
for compare.evol.rates
o New function interlmkdist to calculate linear distances between landmarks

Other changes are listed in the News file.

As always, the most up to date version of geomorph may be found on Github: 
https://github.com/geomorphR/geomorph

Best,

Dean, Mike, Emma, and Antigoni (the geomorph team)

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] geomorph 3.0.4 now available

[Adams, Dean [EEOBS]]

Hello all,

Just a quick announcement to let you know that geomorph version 3.0.4 is now 
available on CRAN. The list of new features is below, and includes:

o An option to use types I, II, or III sums of squares and cross-products for 
ANOVA analyses (e.g., procD.lm, procD.pgls)
o PGLS option for advanced.procD.lm
o An option to choose between SS, F-values, or Cohen's f-squared to 
calculate effect sizes for ANOVA analyses (where applicable: see help files).
o Effect sizes are now centered on mean values from distributions of random 
statistics.  Statistics are also log-transformed in certain cases to normalize 
distributions.
o An option to use only an intercept as a model for procD.lm and 
procD.pgls, with limited output.
o An option to use permutation tests when comparing net evolutionary rates 
for compare.evol.rates
o New function interlmkdist to calculate linear distances between landmarks

Other changes are listed in the News file.

As always, the most up to date version of geomorph may be found on Github: 
https://github.com/geomorphR/geomorph

Best,

Dean, Mike, Emma, and Antigoni (the geomorph team)

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] Re: number of landmarks and sample size

[Adams, Dean [EEOBS]]

Will,

I’m not quite sure what over-parameterizing means in the case of PCA, as it is 
simply a rigid-rotation of the dataspace and does not provide parameters for 
statistical inference.

As for the distribution of eigenvalues, this of course is based on the 
underlying covariance matrix for the traits, which in turn will be affected by 
sample size. However, when traits become even mildly correlated (as is 
certainly the case for landmark coordinates), the distribution of eigenvalues 
of the covariance matrix becomes much better behaved. Specifically, the 
eigenvalues associated with low and high PC axes are less extreme than is 
observed with uncorrelated traits. That implies greater stability in their 
estimation, as the covariance matrix is further from singular (see the large 
statistical literature on the condition of a covariance matrix and subsequent 
estimation issues for ill-behaved covariance matrices).

Best,

Dean


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: William Gelnaw [mailto:wgel...@gmail.com]
Sent: Wednesday, May 31, 2017 5:41 PM
To: mitte...@univie.ac.at
Cc: MORPHMET 
Subject: Re: [MORPHMET] Re: number of landmarks and sample size

I'm currently working on a paper that deals with the problem of 
over-parameterizing PCA in morphometrics.  The recommendations that I'm making 
in the paper are that you should try to have at least 3 times as many samples 
as variables.  That means that if you have 10 2D landmarks, you should have at 
least 60 specimens that you measure.  Based on simulations, if you have fewer 
than 3 specimens per variable, you quickly start getting eigenvalues for a PCA 
that are very different from known true eigenvalues.  I did a literature survey 
and about a quarter of morphometrics studies in the last decade haven't met 
that standard.  A good way to test if you have enough samples is to do a 
jackknife analysis.  If you cut out about 10% of your observations and still 
get the same eigenvalues, then your results are probably stable.
  I hope this helps.
  - Will

On Wed, May 31, 2017 at 1:31 PM, 
mitte...@univie.ac.at 
> wrote:
Adding more (semi)landmarks inevitably increases the spatial resolution and 
thus allows one to capture finer anatomical details - whether relevant to the 
biological question or not. This can be advantageous for the reconstruction of 
shapes, especially when producing 3D morphs by warping dense surface 
representations. Basic developmental or evolutionary trends, group structures, 
etc., often are visible in an ordination analysis with a smaller set of 
relevant landmarks; finer anatomical resolution not necessarily affects these 
patterns. However, adding more landmarks cannot reduce or even remove any 
signals that were found with less landmarks, but it can make ordination 
analyses and the interpretation distances and angles in shape space more 
challenging.

An excess of variables (landmarks) over specimens does NOT pose problems to 
statistical methods such as the computation of mean shapes and Procrustes 
distances, PCA, PLS, and the multivariate regression of shape coordinates on 
some independent variable (shape regression). These methods are based on 
averages or regressions computed for each variable separately, or on the 
decomposition of a covariance matrix.

Other techniques, including Mahalanobis distance, DFA, CVA, CCA, and relative 
eigenanalysis require the inversions of a full-rank covariance matrix, which 
implies an access of specimens over variables. The same applies to many 
multivariate parametric test statistics, such as Hotelling's T2, Wilks' Lambda, 
etc. But shape coordinates are NEVER of full rank and thus can never be 
subjected to any of these methods without prior variable reduction. In fact, 
reliable results can only be obtained if there are manifold more specimens than 
variables, which usually requires variable reduction by PCA, PLS or other 
techniques, or the regularization of covariance matrices (which is more common 
in the bioinformatic community).

For these reasons, I do not see any disadvantage of measuring a large number of 
landmarks, except for a waste of time perhaps. If life time is an issue, one 
can optimize landmark schemes as suggested by Jim or Aki.

Best,

Philipp

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RE: [MORPHMET] Error in estimate.missing (package geomorph)

[Adams, Dean [EEOBS]]

Pere,

I agree that there is likely some issue in the data array and how it is 
configured, but it is difficult to troubleshoot without knowing more and seeing 
the input data.

If you communicate with me off-line I am willing to take a look for you.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Pere Ibáñez [mailto:perei...@gmail.com]
Sent: Tuesday, January 31, 2017 5:34 AM
To: MORPHMET 
Subject: [MORPHMET] Error in estimate.missing (package geomorph)

I have an array (p x k x n) of landmark corrdinates for a set of specimens. 
Some of them have missing landmarks (NA). I am trying to estimate these 
landmarks using:

estimate.missing(nameofmyarray, method = "Reg")

And I get this error:

Error in incomplete[lndmk, , i] <- tmp : replacement has length zero

Any ideas of what is happening? I followed the exact same procedure with other 
arrays of landmarks and it worked. There is probably something wrong with this 
array, but I have no idea of what...

Thanks!
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RE: [MORPHMET] question about raw tps files in R

[Adams, Dean [EEOBS]]

Marcelo,

If your TPS file contains a scale for each specimen, this information is used 
when reading the landmarks. In other words the specimens are scaled by the 
scale values you have specified in your file, placing them in the units you 
have specified rather than pixel units.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: marcelo cardillo [mailto:marcelo.cardi...@gmail.com]
Sent: Wednesday, January 25, 2017 3:39 PM
To: MORPHMET 
Subject: [MORPHMET] question about raw tps files in R

Hi, I noted that R command redland.tps (of geomorph package) change raw tps 
files while open it, (scaling?). I don´t have a  clear idea about how and why 
the program do it..please someone have an idea about it?

   thank in advance!

 M




--
Dr.Marcelo Cardillo
IMHICIHU-CONICET-UBA
Saavedra 15, 5to piso cp (1083)
Teléfono: (011) 4953-8548 - Interno 212
Buenos Aires. Capital Federal. Argentina
http://conicet.academia.edu/MarceloCardillo
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RE: [MORPHMET] allometry adjustment in R

[Adams, Dean [EEOBS]]

Jin Yung,

Evaluating Rsq for multivariate data can be a bit tricky, as our intuition 
tends to gravitate to ‘what is a large Rsq’ and ‘what is a small Rsq’ in the 
univariate context.  To interpret the importance of a model effect in 
multivariate models, I much prefer a formal effect size.

We (Collyer, Sekora and Adams 2015: Heredity) described a Z-score approach for 
helping one intuit the strength of a model effect. If the Z-score for a 
particular term in the model is large, then there is a large effect on the 
patterns in the data, implying it is important to interpret biologically.  
Examples in the Collyer et al paper show their use for anova models (a related 
method is found in Adams and Collyer 2016).

In your case, I suggest that you first evaluate the Z-score for the interaction 
term, rather than its Rsq, to determine whether this statistically significant 
interaction is also biologically important. These are provided in the 
Procrustes ANOVA tables in geomorph (see help file).

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834

From: Wong Jin Yung [mailto:wongjiny...@gmail.com]
Sent: Tuesday, December 13, 2016 9:54 PM
To: Adams, Dean [EEOBS] <dcad...@iastate.edu>
Cc: MORPHMET <morphmet@morphometrics.org>
Subject: Re: [MORPHMET] allometry adjustment in R

Dear Dean,
Thank you for pointing out my conceptual mistake and explaining it in a way 
easy to understand. For my data, I think the interaction is not biologically 
meaningful, so I do not intend to further investigate the difference in 
allometric trajectories.

Following discussion is for everyone, I will elaborate more and see if people 
here are interested on my specific case -- maybe my case could help other 
students like me too: I have two groups, A & B. A has strong allometry but B 
does not (I tried running regression of shape~size on A & B separately, B gave 
insignificant result and low Rsq -- which is the reason why I have no interest 
on difference in allometric trajectories). But when I ran the regression on the 
full dataset with shape~size*group, I got significant size:group term with low 
Rsq (0.03) and significant HOS test with large angle. Shape prediction from 
common slope (with a simple shape~size on full dataset) did not account for the 
true allometric changes of group A (from separate shape~size for A) like what 
Dean said but did captured most of it, and is off for group B (but then group B 
has little & non-significant allometric changes...). Any further opinion will 
be appreciated, many thanks.

Best,
Jin Yung

On Tue, Dec 13, 2016 at 10:28 PM, Adams, Dean [EEOBS] 
<dcad...@iastate.edu<mailto:dcad...@iastate.edu>> wrote:
Jin Yung,

Before sorting out an R-code equivalent for what MorphoJ does, I recommend that 
you revisit and re-read the last few posts on Morphmet concerning statistical 
and biological significance of comparisons of slopes.  The important thing to 
determine first and foremost is whether or not the significant interaction term 
in your model is biologically meaningful (ie, does it represent the underlying 
patterns in the data or is it spurious for some reason). If it does represent 
the underlying patterns in  your data, than any pooled-group or common 
regression-based allometric residuals makes no sense.

Think of it this way: both a common slope model and a pooled within-group model 
assume that patterns of within-group covariation are equivalent (‘the same’ in 
vernacular speech).  If that assumption can be made, then one can fit a common 
slope or pooled-within group model and obtain so-called allometry-free 
residuals.  However, if the groups really do have different slopes, that means 
the within-group allometric trajectories are different. In this case it makes 
no sense to obtain allometric residuals from a common slope model, or a 
pooled-within group model, as these models do not account for the different 
shape~size relationships in each group. Thus, the common-slope analysis is not 
accounting for the actual allometric patterns in any of the groups. What that 
means is that the residuals for group A are not residuals from the shape~size 
allometric relationship in group A, but rather are the allometric residuals of 
this group +/- the difference in that groups’ slope versus the common-slope 
model. And likewise for group B, group C, etc.   Importantly, note that the 
difference in actual within-group residuals from common-slope residuals is not 
a constant across groups: meaning that there is no way to make these residuals 
comparable units for downstream analyses.

Additionally, obtaining residuals from a separate slopes model (i.e., a model 
containing the interaction term, as you have in your post) also makes little 
sense because here the residu

RE: [MORPHMET] allometry adjustment in R

[Adams, Dean [EEOBS]]

Jin Yung,

Before sorting out an R-code equivalent for what MorphoJ does, I recommend that 
you revisit and re-read the last few posts on Morphmet concerning statistical 
and biological significance of comparisons of slopes.  The important thing to 
determine first and foremost is whether or not the significant interaction term 
in your model is biologically meaningful (ie, does it represent the underlying 
patterns in the data or is it spurious for some reason). If it does represent 
the underlying patterns in  your data, than any pooled-group or common 
regression-based allometric residuals makes no sense.

Think of it this way: both a common slope model and a pooled within-group model 
assume that patterns of within-group covariation are equivalent (‘the same’ in 
vernacular speech).  If that assumption can be made, then one can fit a common 
slope or pooled-within group model and obtain so-called allometry-free 
residuals.  However, if the groups really do have different slopes, that means 
the within-group allometric trajectories are different. In this case it makes 
no sense to obtain allometric residuals from a common slope model, or a 
pooled-within group model, as these models do not account for the different 
shape~size relationships in each group. Thus, the common-slope analysis is not 
accounting for the actual allometric patterns in any of the groups. What that 
means is that the residuals for group A are not residuals from the shape~size 
allometric relationship in group A, but rather are the allometric residuals of 
this group +/- the difference in that groups’ slope versus the common-slope 
model. And likewise for group B, group C, etc.   Importantly, note that the 
difference in actual within-group residuals from common-slope residuals is not 
a constant across groups: meaning that there is no way to make these residuals 
comparable units for downstream analyses.

Additionally, obtaining residuals from a separate slopes model (i.e., a model 
containing the interaction term, as you have in your post) also makes little 
sense because here the residuals are obtained from different allometric 
trajectories for each group.  Thus, those residuals are not comparable, because 
they have accounted for a different shape~size relationship for each group. In 
other words, they are residuals of different models for each group, and are not 
comparable units for subsequent analyses either.

Thus it is ONLY the case that allometric residuals can be used in downstream 
analyses when those groups are deemed to have a common allometric slope.  When 
they do not, allometric residuals from a common model or separate models, 
should not be placed together and used in subsequent analyses.

What I’m getting at here is that when the allometric slopes between groups 
truly are different, then biology has changed your question. The question now 
is in understanding how those allometric trajectories differ, not whether the 
groups differ in the allometric residuals themselves. That is not always what 
the biologist sets out to ask, but sometimes the data tell us what we should be 
asking. Data has a funny way of refocusing our gaze to the questions we should 
have asked!

‘The data are’ as the great Dr. Sokal told many of us over the years.

Hope this helps.

Best,

Dean


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: wongjinyung [mailto:wongjiny...@gmail.com]
Sent: Tuesday, December 13, 2016 7:53 AM
To: MORPHMET 
Subject: [MORPHMET] allometry adjustment in R

Dear all,

My goal is to carry out allometry adjustment to produce 'allometry-free' shapes 
and visualize the result. This has been brought up a lot recently, but I still 
wish to check whether what I did was correct, both in terms of concepts and 
implementation.

Currently I'm doing allometry adjustment in R in this way (without taking group 
into consideration):

mod1 <- lm(shape ~ size)
mean.shape <- apply(shape, 2, mean)
allometry.free.shape <- t(apply(mod1$residuals, 1, `+`, mean.shape))

But I also have a factor (let's call it 'group'). When I run Procrustes ANOVA 
using procD.lm in geomorph, both group and size:group terms are significant, 
and HOS test in procD.allometry is significant too. If I want to take this into 
consideration to get the allometry free shape, what can I do?

I came across MorphoJ's procedure of pooled within group regression and I think 
maybe this is what I am looking for (side note: I'm very confused on the 
difference between what I read from the discussion on this forum some time 
ago
 and MorphoJ's explanation on pooled within group regression, I am not good in 
stats). Following is the explanation from MorphoJ's user 

RE: brief comment on non-significance Re: [MORPHMET] procD.allometry with group inclusion

[Adams, Dean [EEOBS]]

Andrea,

I agree that one must consider both statistical significance and biological 
meaningfulness in evaluating patterns.  Considering one of these without the 
other can often get one into trouble.

Your post concerned the inability to statistically detect differences due to 
sample size limitations, and the possibility of concluding homogeneity from 
this result when it may not be the case. But as Mike mentioned, the opposite is 
also a concern. In fact, one might recall a discussion some months ago on 
Morphmet on this very issue; where large samples afforded the ability to 
discern allometric differences between groups, but where those statistical 
differences may not be biologically important. In both cases, critical thinking 
and a merger of statistical result and biological knowledge of the system are 
required to arrive at a well-reasoned understanding of the patterns in the data.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Mike Collyer [mailto:mlcoll...@gmail.com]
Sent: Monday, December 12, 2016 8:34 AM
To: andrea cardini 
Cc: morphmet@morphometrics.org
Subject: Re: brief comment on non-significance Re: [MORPHMET] procD.allometry 
with group inclusion

Andrea,

My opinion on this is that the researcher who has collected the data must 
retain at all times a biological wisdom that supersedes a suggested course of 
action based on results from a statistical test.  If the purpose of a study is 
to assess the allometric pattern of shape variation within populations, then 
maybe the results of a homogeneity of slopes test can be an unnecessary burden. 
 If a researcher wants to compare the mean shapes of different groups but is 
concerned that allometric variation might differ among groups, then a 
homogeneity of slopes test could be an important first step, but I agree that a 
non-significant result should not spur the researcher to immediately conclude a 
common allometry or no allometry is appropriate.  Sample size, variation in 
size among groups, and appropriate distributions of specimen size within groups 
might all be things to think about.

The point you make about a potential type II error is a real concern.  The 
opposite problem is also a real concern.  One might have very large sample 
sizes and sufficient statistical power to suggest that allometric slopes are 
heterogeneous.  However, the coefficient of determination and/or effect size 
for size:group interaction might be quite small.  Just because there is a low 
probability of finding as large of an effect based on thousands of random 
permutations, is one ready to accept that different groups have evolved unique 
allometric trajectories?  It is easy to forget that the choice of “significance 
level” - the a priori acceptable rate of type I error - is arbitrary.  Making 
strong inferential decisions based on a binary decision for an arbitrary 
criterion is probably not wise.  I would argue that instead of focusing on a 
P-value, one could just as arbitrarily, but perhaps more justifiably, choose a 
coefficient of determination of R^2 = 0.10 or an effect size of 2 SD as a 
criterion for whether to retain or omit the interaction coefficients that allow 
for heterogenous slopes.

*** Warning: pedantic discussion on model selection starts here.  Skip if 
unappealing.

One could also turn to model selection approaches.  However, I think 
multivariate generalization for indices like AIC is an area lacking needed 
theoretical research for high-dimensional shape data.  There are two reasons 
for this.  First, the oft-defined AIC is model log-likelihood + 2K, where K is 
the number of coefficients in a linear model (rank of the model design matrix) 
+ 1, where the 1 is the dimension of the value for the variance of the error.  
This is a simplification for univariate data.  The second half of the equation 
is actually 2[pk + 0.5p(p+1)], where p is the number of shape variables and k 
is the rank of the design matrix.  (One might define p as the rank of the shape 
variable matrix - the number of actual dimensions in the tangent space, also 
equal to the number of principal components with positive eigen values greater 
than 0 from a PCA - if using high-dimensional data or small samples.)  Notice 
that substituting 1 for p in this equation gets one back to the 2K, as defined 
first.  The pk part of the equation represents the dimensions of linear model 
coefficients; the 0.5p(p+1) part represents the dimensions of the error 
covariance matrix.  The reason this is important is that one might have picked 
up along the way that a delta AIC of 1-2 means two models are comparable (as if 
with equal likelihoods, they differ by around 1 parameter or less).  This rule 
of thumb would have to be augmented with highly 

RE: [MORPHMET] Re: the problem with CVA... or is it?

[Adams, Dean [EEOBS]]

Christy,

If your model is: shape~eco.group, then the LS means for each ecological group 
can be extracted. These can then be examined visually by using TPS from the 
overall reference to each LS mean.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Christy Hipsley [mailto:chips...@museum.vic.gov.au]
Sent: Monday, November 28, 2016 6:44 PM
To: MORPHMET 
Subject: [MORPHMET] Re: the problem with CVA... or is it?

Dean - so in this case how would I use the LS means for each group (here I have 
only one factor and no slope) as the coordinates for the target specimen and 
the mean shape for all species as the reference?

Sorry I should have been more clear - the CVA was done using individual shapes, 
so n=161, and the bgPCA was on species means (the basic unit of my study), so 
n=92. I did the CVA on the individuals so as not to have more "groups" than 
variables and avoid false separation. I've seen your bat paper and indeed 
thought of doing something similar. I just liked the CVA because it showed very 
well the environmental gradient along which the different cranial shapes fall.

On Tuesday, November 29, 2016 at 10:04:02 AM UTC+11, Christy Hipsley wrote:
Dear Morphmet-ers,

I'm seeking advice on methods for visualizing shape features that distinguish 
multiple groups using GM. I know CVA has fallen out of favor for a number of 
reasons discussed here - e.g., more variables than groups, nonisotropic 
variation:

Mitteroecker, P., and Bookstein, F. 2011. Linear discrimination, ordination, 
and the visualization of selection gradients in modern morphometrics. Evol. 
Biol. 38:100–114.
Klingenberg, C. P., and Monteiro, L. R. 2005. Distances and directions in 
multidimensional shape spaces: Implications for morphometric applications. 
Syst. Biol. 54:678–688.

Although given these limitations, is it really expected to give completely 
false results regarding the visualization of shape changes? In my study sytem, 
I show that ecological groups have statistically different cranial shapes, 
using both Procrustes ANOVA and PGLS. Now I simply want to visualize what the 
main features are that distinguish them, preferably using warps or wireframes, 
so that those changes must be directly relateable to the original landmark 
coordinates. I did that using individual specimens instead of species means, so 
I have 161 individuals vs 144 variables (48 landmarks*3D). I also did a 
between-group PCA on the species means which shows the same pattern, so is it 
technically "wrong" to show both?

Thanks for any feedback on this issue, and I would appreciate to hear any 
alternative methods that people might use. I use MorphoJ and Geomorph for 
analyses.

Best,
Christy
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RE: [MORPHMET] Measurements to Landmarks

[Adams, Dean [EEOBS]]

Edgar,

That would depend on how the measurements intersect with one another. For a 
series of truss-like measurements, it is possible. See the paper by Carpenter 
et al. 1996 in the white book.

If I recall correctly, this, and a related approach was implemented in 
Morpheus, though I have not investigated that recently to know if it is in the 
current version.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Edgar Esteban Herrera Collazos [mailto:edgaresteba...@gmail.com]
Sent: Tuesday, November 8, 2016 7:27 PM
To: morphmet@morphometrics.org
Subject: [MORPHMET] Measurements to Landmarks

Hey everyone,

Just wondering, I have a sets of measurements, and I was wondering if there is 
a way to turn those measurements into landmarks?

Best regards,

--
Edgar Esteban Herrera Collazos B.Sc.
Cel: 319 379 0213

Postgraduate Student at Pontificia Universidad Javeriana
Ichthyology Laboratory 108B Ed 53 - 3208320 Ext 4127
---
Estudiante de Posgrado en la Pontificia Universidad Javeriana
Laboratorio de Ictiología 108B Ed 53 - 3208320 Ext 4127


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[MORPHMET] RE: [geomorph-r-package] Re: geomorph updates

[Adams, Dean [EEOBS]]

Milena,

Installing from Github requires installing from the stable branch, not Master. 
Try this:

devtools::install_github("geomorphR/geomorph", ref = "Stable")


Note the ‘Stable’ option at the end.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: geomorph-r-pack...@googlegroups.com 
[mailto:geomorph-r-pack...@googlegroups.com] On Behalf Of Milena Stefanovic
Sent: Friday, October 28, 2016 8:57 AM
To: geomorph R package 
Cc: morphmet@morphometrics.org
Subject: [geomorph-r-package] Re: geomorph updates


It didn't work for me:

Downloading GitHub repo geomorphR/geomorph@master from URL 
https://api.github.com/repos/geomorphR/geomorph/zipball/master
Error in stop(github_error(request)) : 404: Not Found
 (404)

I am using R Studio Version 0.99.903 for Windows.

Best regards,
Milena
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RE: [MORPHMET] Question about size adjusted residuals in Geomorph

[Adams, Dean [EEOBS]]

Edgar,

If you perform a multivariate regression of shape on size (shape~size), the 
residuals from this regression represent that shape component not explained by 
size variation and the relationship between the two. In the literature, this is 
commonly referred to as size-adjusted residuals or allometry free shape.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Edgar Esteban Herrera Collazos [mailto:edgaresteba...@gmail.com]
Sent: Sunday, October 9, 2016 8:46 AM
To: morphmet@morphometrics.org
Subject: [MORPHMET] Question about size adjusted residuals in Geomorph

Hi everyone, I'm working in Geomorph package in R and I have a question 
regarding working with allometric effect.

What is the difference between size-adjusted residuals and allometry free 
shapes.

Thanks in advance
--
Edgar Esteban Herrera Collazos B.Sc.
Cel: 319 379 0213

Postgraduate Student at Pontificia Universidad Javeriana
Ichthyology Laboratory 108B Ed 53 - 3208320 Ext 4127
---
Estudiante de Posgrado en la Pontificia Universidad Javeriana
Laboratorio de Ictiología 108B Ed 53 - 3208320 Ext 4127


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RE: [MORPHMET] Re: allometry and phylogeny

[Adams, Dean [EEOBS]]

Christy,

Yes, the ‘slopes’ component of the output contains the regression parameters 
for each species. These can then be used as input for ancestral state 
estimation and other down-stream analyses that you have planned.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Christy Hipsley [mailto:chips...@museum.vic.gov.au]
Sent: Tuesday, September 6, 2016 2:00 AM
To: MORPHMET 
Cc: damien.esque...@anu.edu.au
Subject: [MORPHMET] Re: allometry and phylogeny

To follow up on Damien's question and Dean's response, which values from the 
advanced.procD.lm results would one use for reconstructing species ontogenetic 
slopes? There are several outputs relating to slope from that test, like 
"slopes",  "slope.lengths", "slopes.dist", "slopes.angles".

I understand these each describe different parameters of the slope vector like 
length and orientation - basically magnitude and direction of shape change per 
unit size. So should one reconstruct ancestral states of each of these each 
separately to compare with tip taxa?

Thanks for any advice,
Christy

On Friday, July 1, 2016 at 12:31:37 AM UTC+10, dcadams wrote:
Damien,

Yes, one can take the regression parameters for each species and use these as 
‘tips’ data to obtain ancestral estimates of the regression slope. In this 
case, the species’ data is the multivariate set of slope coefficients for each 
multivariate regression. Thus, the ancestral state of the allometry is also a 
multivariate set of parameters.

Since you are using geomorph the slopes for each species’ allometry trajectory 
may be found in the output list from advanced.procD.lm.

Best,

Dean


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Damien Esquerre Gheur [mailto:damien@anu.edu.au]
Sent: Thursday, June 30, 2016 6:50 AM
To: morp...@morphometrics.org
Subject: [MORPHMET] allometry and phylogeny


Dear morphometricians,



I have a pretty big dataset of specimens for most species of pythons 
encompassing most of the size range for every species. I have been projecting 
the allometric trajectories and doing pairwise slope comparisons and things 
like that in Geomorph. However, it seems very obvious that the is a very strong 
phylogenetic signal in the data, as species within the same clade tend to have 
parallel trajectories.

I have been trying to find a way to analyse this data in a phylogenetic 
framework, and so far I know there is no straightforward solution. I know in 
Adams and Nistri 2010 the trajectories where coded ad isometric and allometric 
and then reconstructed the ancestral states but the case it not so binary with 
my data.

Would there be a way, for example, to extract a value for the slope and do an 
ancestral state reconstruction on that? Also, is there a way to incorporate 
phylogeny in slope comparison models?

Any help would be greatly appreciated


Damien Esquerré
PhD Student, Keogh Lab
Division of Evolution, Ecology and Genetics, Research School of Biology
The Australian National University
44 Daley Road, ACTON ACT 2601, Australia

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[MORPHMET] geomorph 3.0.2 on CRAN

[Adams, Dean [EEOBS]]

Colleagues,

I wish to announce that geomorph 3.0.2 is now up on CRAN.  This upgrade 
includes a number of enhancements and features, and a few minor bug fixes, as 
outlined in the News file of the program.   As always, the most up to date 
version of geomorph may also be found on Github.

Best,

Dean and the Geomorph Team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] RE: allometry and phylogeny

[Adams, Dean [EEOBS]]

Damien,

Yes, one can take the regression parameters for each species and use these as 
‘tips’ data to obtain ancestral estimates of the regression slope. In this 
case, the species’ data is the multivariate set of slope coefficients for each 
multivariate regression. Thus, the ancestral state of the allometry is also a 
multivariate set of parameters.

Since you are using geomorph the slopes for each species’ allometry trajectory 
may be found in the output list from advanced.procD.lm.

Best,

Dean


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Damien Esquerre Gheur [mailto:damien.esque...@anu.edu.au]
Sent: Thursday, June 30, 2016 6:50 AM
To: morphmet@morphometrics.org
Subject: [MORPHMET] allometry and phylogeny


Dear morphometricians,



I have a pretty big dataset of specimens for most species of pythons 
encompassing most of the size range for every species. I have been projecting 
the allometric trajectories and doing pairwise slope comparisons and things 
like that in Geomorph. However, it seems very obvious that the is a very strong 
phylogenetic signal in the data, as species within the same clade tend to have 
parallel trajectories.

I have been trying to find a way to analyse this data in a phylogenetic 
framework, and so far I know there is no straightforward solution. I know in 
Adams and Nistri 2010 the trajectories where coded ad isometric and allometric 
and then reconstructed the ancestral states but the case it not so binary with 
my data.

Would there be a way, for example, to extract a value for the slope and do an 
ancestral state reconstruction on that? Also, is there a way to incorporate 
phylogeny in slope comparison models?

Any help would be greatly appreciated


Damien Esquerré
PhD Student, Keogh Lab
Division of Evolution, Ecology and Genetics, Research School of Biology
The Australian National University
44 Daley Road, ACTON ACT 2601, Australia

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RE: [MORPHMET] Form Space?

[Adams, Dean [EEOBS]]

Ariadne,

The remaining components of the procedure in Mitteroecker et al. (2004) involve 
calculating the common allometric and residual shape components (CAC & RSC 
respectively), and then visualizing allometric trends using a series of plots: 
CAC vs. size, RSC1 vs. CAC, etc.  While the plot of size-shape space Carmelo 
mentioned is not 'canned' in an R function as far as I am aware, these latter 
components of the procedure are available in geomorph's plotAllometry function.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

-Original Message-
From: Carmelo Fruciano [mailto:c.fruci...@unict.it] 
Sent: Monday, April 4, 2016 3:22 AM
To: morphmet@morphometrics.org
Subject: Re: [MORPHMET] Form Space?

Ariadne Schulz  ha scritto:

> Hello All,
>
> I have another rather silly question that I'm hoping either has a very 
> simple answer or will set off another wave of debate. I'm still on 
> about my 3D semilandmarks and now I want to plot them in form space in 
> R. I have my original landmarks and my GPA coordinates and centroid 
> size from gpagen with a sliding function, and so forth so the new 
> version of plotAllometry should work, but I really want to plot in 
> form space. It seems to me I'm just missing something really easy. Is there a 
> way to do this?

Dear Ari,
if you are referring to plots of principal components/relative warps in 
"size-shape space", as defined by Mitteroecker et al 2004 (Journal of Human 
Evolution), it is basically a PCA on Procrustes coordinates + log centroid 
size. Reading of the original paper is also useful to understand the idea and 
its application.

I don't know if this approach is implemented "out of the box" in some R package 
but, given its simplicity, it's easily obtained referring to the original 
publication by using even basic R function or other general-purpose statistical 
software.

You can see an example of these plots in one of my own papers (Fruciano et al 
2012 - Environmental Biology of Fishes) and, really, in many others, where it 
is used to produce exploratory plots.

I hope this helps.
Best,
Carmelo

--
Carmelo Fruciano
Postdoctoral Fellow - Queensland University of Technology - Brisbane, Australia 
Honorary Fellow - University of Catania - Catania, Italy e-mail 
c.fruci...@unict.it http://www.fruciano.it/research/

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[MORPHMET] geomorph 3.0.1 on CRAN

[Adams, Dean [EEOBS]]

Colleagues,

We wish to announce that geomorph 3.0.1 is now uploaded to CRAN. This version 
matches the version already found on github. This version contains the minor 
enhancements and changes made since the release of 3.0.0. The News page details 
all changes made.

As always, geomorph may be installed from CRAN, or from Github. For the latter, 
the command is:

devtools::install_github("geomorphR/geomorph", ref="Stable")

Best,

Dean and the geomorph team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] using regression residuals for other analyses

[Adams, Dean [EEOBS]]

Hi Andrea,

It is generally preferable to perform the more complex analysis with size 
included as a covariate.  Using a sequential approach that first obtains the 
shape residuals and then examines patterns using these as data is not 
guaranteed to get to the same, or even the correct, place.  And this approach 
can leave potentially important biology out. 

Consider the simplest case with shape, size, and groups (i.e., mancova). Here 
the full model is: shape~size+group+size:group. Say for instance, that one 
finds a significant interaction term. This means that the groups have different 
shape~size relationships (ie different allometric slopes). In this case, using 
residuals from a shape~size regression in a subsequent manova is not correct, 
as these are residuals from a common-slope model, whereas the mancova has found 
evidence that the groups have different slopes. Thus the residuals are not 
capturing what one intends. (As a side note there was a nice paper in the 
mid-1990s on the univariate equivalent of this, describing why anova of 
regression residuals is not the same as ancova). 

But additionally, using the sequential-analysis approach eliminates the 
possibility of identifying interesting interactions between effects that one 
had not considered. Again take this simple example. Here, performing a manova 
on the regression residuals is intended to evaluate differences in the mean 
shapes among groups. But this explicitly ignores the possibility that the 
groups may differ in their allometries themselves, rather than their 
size-adjusted least squares means. Such allometric differences represent 
potentially important biological information that is left unexplored using the 
piecewise analysis procedure.

For these reasons the analysis including size as a covariate is preferred. And 
while it is more complicated to consider models that include interactions, and 
various post-hoc comparisons are required (of group means, of slopes, etc.), 
one ought to do so when possible, so as to properly identify where patterns of 
shape variation occur, and what potential factors associate with it. 

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834


-Original Message-
From: andrea cardini [mailto:alcard...@gmail.com] 
Sent: Thursday, March 24, 2016 12:01 PM
To: morphmet@morphometrics.org
Subject: [MORPHMET] using regression residuals for other analyses

Dear All,
this is something that, I believe, has already come up in the past. 
However, I'd like to check it again.

What are the issues with, say, regressing shape on size, saving residuals and 
using those in further analyses (e.g., other regressions or testing group 
differences etc.)?

I suspect that all the factors (size, other predictors, groups etc.) should be 
incorporated in a single model and may have a partial intuition about some of 
the problems with rerunning, instead, analyses on residuals but I'd be very 
grateful to know how those with a better understanding of the methods see it.

Thanks in advance.
Cheers

Andrea


-- 

Dr. Andrea Cardini
Researcher, Dipartimento di Scienze Chimiche e Geologiche, Università di Modena 
e Reggio Emilia, Via Campi, 103 - 41125 Modena - Italy tel. 0039 059 2058472

Adjunct Associate Professor, Centre for Forensic Science , The University of 
Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia

E-mail address: alcard...@gmail.com, andrea.card...@unimore.it
WEBPAGE: https://sites.google.com/site/alcardini/home/main


FREE Yellow BOOK on Geometric Morphometrics: 
http://www.italian-journal-of-mammalogy.it/public/journals/3/issue_241_complete_100.pdf

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[MORPHMET] small update to geomorph

[Adams, Dean [EEOBS]]

Colleagues,



We have made a small change to geomorph that affects the sliding of surface 
points during GPA.  The latest version may be found on Github:



devtools:::install_github("geomorphR/geomorph", ref = "Stable")

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] small update to geomorph

[Adams, Dean [EEOBS]]

Friends,

A small update was made to geomorph, and may be found on our Github repository. 
One change fixes an issue with outputting the scale in digitize2d, and another 
change was made to allow 3+ partitions in phylo.integration.  Thanks to Melanie 
Hopkins and Bryan McLean for identifying these issues.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] Questions regarding correction for allometry & evolutionary allometry

[Adams, Dean [EEOBS]]

Nicole,

There is no need to perform multiple residual-based analyses. If you wish to 
obtain shape residuals where both the phylogeny and allometry (size) have been 
taken into account, these are found as residuals from the PGLS analysis: 
shape~size|phylogeny.

However, a question then is what will these size/phylogeny shape residuals be 
used to investigate?  If the intention is to then evaluate these relative to 
some other factor (say groups), then the correct approach is just to perform a 
factorial PGLS analysis, where: shape~size+factor | phylogeny (shape is a 
function of size and some other factor, given the phylogeny). Additionally, if 
that factor describes groups, you may wish to include the size:group 
interaction term.

In fact, if that is indeed the case, it is advisable NOT to perform the 
analysis in piecemeal fashion, where residuals from one regression are then 
used in a subsequent linear model to test other effects. The reason is that if 
there is some interaction between model effects (say, between size and groups), 
then the residuals from the first regression are not correctly capturing the 
observed patterns of variation.  This is the multivariate equivalent of the 
ANCOVA problem, and why an ANOVA on residuals from a regression is not always 
the same as performing the ANCOVA analysis. The best solution is to simply 
perform the factorial model, and account for size while examining other 
effects.  For this correct approach, one simply requires software that allows 
one to perform factorial PGLS. Geomorph (and as I recall, NTSYS) will allow 
uesrs to perform factorial PGLS.

Hope this is helpful.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Nicole Dzenowski [mailto:ndzenow...@gmail.com]
Sent: Monday, February 1, 2016 9:18 PM
To: MORPHMET 
Subject: [MORPHMET] Questions regarding correction for allometry & evolutionary 
allometry


Hi everyone,

I had some questions regarding allometry correction. I'll be working with 
closely related specimens on a project where I think a moderate chunk of the 
shape variation is due to allometric size differences. Should (or can) I 
correct for both, as in, regress shape on size and take the residuals and then 
use a phylogenetic comparative method on those residuals and the size data and 
then do another multivariate regression and then use the residuals from that 
final regression as my new shape variables?

Any help or direction is greatly appreciated.

Thanks!
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[MORPHMET] geomorph 3.0 released

[Adams, Dean [EEOBS]]

Colleagues,

I am very excited to announce the release of geomorph 3.0. This version 
represents an entire rewrite of the analytical functions and their underlying 
support code, which has resulted in improved speed, performance, and 
flexibility of the package.  We are especially grateful to those who served as 
beta-testers prior to release, and who identified potential problems, and 
provided many useful suggestions.
Geomorph 3.0 has many new features that may be of interest to our users. This 
includes a suite of modularity and integration functions, new test statistics 
for some analyses, an option to use nested effects in ANOVA, a data frame 
generation function to ensure input data compatibility, as well as new plotting 
options.

Geomorph 3.0 is intended to function in much the same manner as the base-R 
package. Thus, one now runs an analysis, views results via 'summary()' and 
obtains plots via 'plot()'; rather than having all output including plots 
produced automatically.  Additionally, each analytical function produces both 
primary and supplementary output, which may be accessed via the list produced 
by that particular function. As a result of these changes, users will notice 
that some options are no longer required: e.g., 'verbose =' and 'ShowPlot =' 
have been eliminate from the analytical functions, and the input parameters for 
a few functions have changed slightly. Please be aware of this, as older 
scripts calling 'verbose' and 'ShowPlot' in particular will need to be 
adjusted. As before, changes to geomorph 3.0 are documented in both the News 
file and in the help files for the individual functions. We suggest that users 
consult these documents for specific instructions on how to run various 
analyses (all analytical functions have R-code examples to provide additional 
guidance).
To obtain geomorph 3.0 one may install the package from CRAN, or from our 
Github repository.  However, we strongly recommend installing from Github, as 
any bug fixes or updates will be made immediately on Git, whereas the CRAN 
version will only be updated every few months as per CRAN policies. 
Additionally, some of the dependent packages called by geomorph have just been 
eliminated in the Git version, which should facilitate installation geomorph. 
Installing from github is accomplished via:

devtools::install_github("geomorphR/geomorph", ref="Stable")

Note: first time users will need to install the 'devtools' package, and may 
need to install Rtools prior to geomorph. The latter may be found at: 
https://cran.rstudio.com/bin/windows/Rtools/ . Additional installation 
instructions may be found on our blogsite: geomorphpackage.blogspot.com/

As always, our goal with geomorph is to provide users with the latest 
analytical procedures based on the current theory of the field. We hope that 
geomorph provides researchers a flexible toolkit for the ever-expanding and 
exciting diversity of applications we are seeing in the geometric morphometric 
community!  Comments and suggestions for improvement may be made via our blog 
page (www.geomorphpackage.blogspot.com), or from emails directly to us. We 
welcome these suggestions as we consider how to enhance geomorph moving forward.
Best regards,

Dean and the geomorph team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] geomorph blog

[Adams, Dean [EEOBS]]

Colleagues,
   We wish to inform you of a location change to our geomorph web 
page and blog.  The blog and associated pages may now be viewed at:  
http://geomorphpackage.blogspot.com. All prior posts are available at this 
location.

Dean and the geomorph team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] RE: R code for CR modularity test

[Adams, Dean [EEOBS]]

Colleagues,

Paolo Piras kindly pointed out that for the 2 group case, the 'fix' I made for 
one issue truncated one line of code, causing an error such that the analysis 
did not complete (embarrassing, but happens occasionally).

The new and corrected code is on my web page: 
http://www.public.iastate.edu/~dcadams/software.html

Thank you Paolo!

Best to all.

Dean


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834

From: Adams, Dean [EEOBS]
Sent: Wednesday, December 2, 2015 9:11 AM
To: morphmet@morphometrics.org
Subject: R code for CR modularity test

Colleagues,

I have received a number of requests for the R code for implementing the CR 
coefficient for evaluating modularity. To accommodate this request I have 
uploaded the basic-code on my software page: 
http://www.public.iastate.edu/~dcadams/software.html . It is method #13, near 
the top of the page.

Note that this code corrects a small bookkeeping error for when the CR is used 
on 4+ modules (as compared to the code found in the Supplemental Material). 
This error did NOT affect significance tests; only the output presentation of 
the CR.observed.

Best to all,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834

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[MORPHMET] R code for CR modularity test

[Adams, Dean [EEOBS]]

Colleagues,

I have received a number of requests for the R code for implementing the CR 
coefficient for evaluating modularity. To accommodate this request I have 
uploaded the basic-code on my software page: 
http://www.public.iastate.edu/~dcadams/software.html . It is method #13, near 
the top of the page.

Note that this code corrects a small bookkeeping error for when the CR is used 
on 4+ modules (as compared to the code found in the Supplemental Material). 
This error did NOT affect significance tests; only the output presentation of 
the CR.observed.

Best to all,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] new modularity method

[Adams, Dean [EEOBS]]

Colleagues,

I wish to alert you to a new paper, currently accepted at Methods in Ecology 
and Evolution, in which I describe a new measure for modularity; called the 
covariance ratio, or CR.  The accepted version of the paper may be found at: 
http://onlinelibrary.wiley.com/doi/10./2041-210X.12511/abstract

In this paper I show some undesirable properties of the popular RV coefficient 
(some known and some previously unknown), and derive an alternative measure, 
the CR coefficient, which is insensitive to these same issues.  I show that the 
CR has higher statistical power relative to the RV for detecting patterns of 
modularity, and discuss why the RV should not be used when comparing modular or 
integrated signals across datasets.

R code for a base implementation of the CR test for modularity is found in the 
supplemental material. The method will also be included in the next update of 
geomorph.

Best to all,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] small update to geomorph on CRAN

[Adams, Dean [EEOBS]]

Colleagues,

Just a quick note to inform you of a geomorph update on CRAN. This version 
(2.1.7-1) contains the fixes previously found on the stable branch of our 
Github repository.  For those who have already updated using Github, there is 
no need to update via CRAN.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] RE: Important update for geomorph

[Adams, Dean [EEOBS]]

Marcela,

Be sure that you have first installed RTools.  These are needed to compile any 
R package from source. You can find these at: 
https://cran.r-project.org/bin/windows/Rtools/


Note that for Mac users, there are additional steps as outlined in the geomorph 
installation page: http://www.geomorph.net/p/installing-geomorph.html

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834

From: Randau, Marcela [mailto:m.ran...@ucl.ac.uk]
Sent: Monday, September 28, 2015 1:30 PM
To: Adams, Dean [EEOBS] <dcad...@iastate.edu>; morphmet@morphometrics.org
Subject: RE: Important update for geomorph

Dear Dr Dean Adams,

Many thanks for sending the information on the new version of geomorph with bug 
fixes.
I have tried to install it using the GitHub commands on your message, however I 
keep running into the same error:

“Warning: running command 'make -f "Makevars.win" -f 
"C:/PROGRA~1/R/R-32~1.2/etc/x64/Makeconf" -f 
"C:/PROGRA~1/R/R-32~1.2/share/make/winshlib.mk" SHLIB="geomorph.dll" WIN=64 
TCLBIN=64 OBJECTS="geomorph.o"' had status 127
ERROR: compilation failed for package 'geomorph'
* removing 'C:/Users/Marcela/Documents/R/win-library/3.2/geomorph'

Would you have any advice on how I should proceed?

Many thanks,

Marcela Randau


From: Adams, Dean [EEOBS] [mailto:dcad...@iastate.edu]
Sent: 28 September 2015 10:45
To: morphmet@morphometrics.org<mailto:morphmet@morphometrics.org>
Subject: [MORPHMET] Important update for geomorph

Colleagues,

We recently uploaded version 2.1.7 of geomorph to CRAN.  However, subsequent to 
this, we found that under certain input scenarios, some of the changes we made 
to underlying support code had undesirable outcomes. We have corrected these 
issues in version 2.1.7-1. The corrected version is found in our GitHub 
repository.

Note that the version currently housed on the CRAN repository (2.1.7) does not 
contain the bug fixes. The reason is that CRAN policies do not permit frequent 
updates to packages, and 2.1.7 was just released last week.

As a consequence, we recommend that going forward, all geomorph users should 
install the latest version from our Github repository.  Beginning immediately, 
we will maintain the current version of geomorph on the ‘Stable’ branch in our 
GitHub repository.  This version represents the most recent release to CRAN 
plus any necessary bug fixes.  See the ReadMe file in Git for more information.

To install geomorph directly from GitHub, go to R and type the following two 
commands:

> install.packages("devtools")
> devtools::install_github("EmSherratt/geomorph", ref="Stable")

Thanks for all of your suggestions for improvements to geomorph. They are 
appreciated.

Best,

Dean and the geomorph team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834

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[MORPHMET] Important update for geomorph

[Adams, Dean [EEOBS]]

Colleagues,

We recently uploaded version 2.1.7 of geomorph to CRAN.  However, subsequent to 
this, we found that under certain input scenarios, some of the changes we made 
to underlying support code had undesirable outcomes. We have corrected these 
issues in version 2.1.7-1. The corrected version is found in our GitHub 
repository.

Note that the version currently housed on the CRAN repository (2.1.7) does not 
contain the bug fixes. The reason is that CRAN policies do not permit frequent 
updates to packages, and 2.1.7 was just released last week.

As a consequence, we recommend that going forward, all geomorph users should 
install the latest version from our Github repository.  Beginning immediately, 
we will maintain the current version of geomorph on the 'Stable' branch in our 
GitHub repository.  This version represents the most recent release to CRAN 
plus any necessary bug fixes.  See the ReadMe file in Git for more information.

To install geomorph directly from GitHub, go to R and type the following two 
commands:

> install.packages("devtools")
> devtools::install_github("EmSherratt/geomorph", ref="Stable")

Thanks for all of your suggestions for improvements to geomorph. They are 
appreciated.

Best,

Dean and the geomorph team

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] new phylogenetic comparative method for shape data

[Adams, Dean [EEOBS]]

Colleagues,

I wish to alert you to a new method John Denton and I just published in 
Evolution. The approach enables one to compare rates of phenotypic evolution 
between two or more multivariate traits in a phylogenetic context under 
Brownian motion.  The method is described in our paper "A new phylogenetic test 
for comparing multiple high-dimensional evolutionary rates suggests interplay 
of evolutionary rates and modularity in lanternfishes (Myctophiformes; 
Myctophidae). Evolution. 69:2425-2440." The paper may be found on my web page 
(publications page).

Additionally, the approach will be implemented in the upcoming version of 
geomorph (2.1.7), which is set for release in the next few weeks. For those who 
cannot wait that long, they may download and install this version from the 
develop branch of the program on Github (instructions at: 
http://www.geomorph.net/p/installing-geomorph.html).

Best to all,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] Suggestions for geomorph

[Adams, Dean [EEOBS]]

Thank you Mauro for the suggestions.  Indeed, as you surmise, digitizing within 
R is not at all straightforward, but we will do our best to improve the 
experience for users of geomorph.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Mauro Cavalcanti [mailto:mauro...@gmail.com]
Sent: Monday, June 8, 2015 3:59 PM
To: MORPHMET
Subject: [MORPHMET] Suggestions for geomorph

Dear members of the geomorph Development Team,
I have been using the digitize2d() function of the geomorph R package to obtain 
landmark coordinates from my fish specimens.It works fine, but I respectfully 
have two suggestions for its improvement.
First one is quite simple: when end of file is reached and the last specimen 
has been digitized, the function stops with one of those ugly and arcane error 
messages (well, all programming languages generate ugly and arcane error 
messages! ;-)). This should not happen; instead, the function should issue a 
user-friendly warning, like End of file, Last specimen (or perhaps 
something less friendly, like Can't you see you have reached the end of 
file, you moron? ;-))
Second suggestion may be more difficult to implement, but if it can be done, it 
would provide a great improvement: the ability to edit a previoulsly created 
file. Currently, the function detects the specimens without landmark 
coordinates and starts digitizing from this specimen on; if all specimens have 
already landmark coordinates, then the function stops with the same ugly error 
messages referred to above. It would be really great if one could see each 
specimen with the superimposed digitized landmarks, one by one, so that 
landmarks could be checked, added, or deleted.
Hope this helps!
With warmest regards,

--
Dr. Mauro J. Cavalcanti
E-mail: mauro...@gmail.commailto:mauro...@gmail.com
Web: http://sites.google.com/site/maurobio
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RE: [R-sig-phylo] [MORPHMET] Re: Stability of p-values (physignal and testing for morphological integration)

[Adams, Dean [EEOBS]]

Yes that is more precise. 

In my post to the query I only noted that the variance in significance levels 
across multiple permutation tests decreases as the number of iterations 
increases. Joe's post provides the equation for the expected value of that 
variance; mine provided reference to an empirical example (Adams and Anthony, 
1996).

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/
phone: 515-294-3834

-Original Message-
From: R-sig-phylo [mailto:r-sig-phylo-boun...@r-project.org] On Behalf Of Joe 
Felsenstein
Sent: Monday, June 8, 2015 1:29 AM
To: Dennis E. Slice; r-sig-phylo mailman
Subject: Re: [R-sig-phylo] [MORPHMET] Re: Stability of p-values (physignal and 
testing for morphological integration)

A number of people have suggested that P values should stabilize after a number 
of samples (in a permutation test) that depends on the data set.

I suspect that these were unintended misstatements.  As Dennis Slice has 
mentioned, one can regard each permutation in the permutation test as a random 
sample from a distribution.  Comparing a test statistic X to its value in the 
data (say, Y), each permutation draws from a distribution in which there is a 
probability P that X exceeds Y.

So each permutation is (to good approximation) a coin toss with probability P 
of Heads.  There obviously no number of tosses beyond which the fraction of 
Heads stabilizes.  The fraction of heads after N tosses will depart from the 
true value P by an amount which has expectation 0, and variance P(1-P)/N.  This 
is a fairly slow approach of the fraction of Heads to the true value.

So to get twice as close to the true P value, one needs 4 times as many 
permutations.  And this need for more and more samples continues indefinitely.  
There is no sudden change as one reaches a threshold number of permutations.

But that's what you really meant, right?

Joe
---
Joe Felsenstein  j...@gs.washington.edu

[[alternative HTML version deleted]]

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RE: [MORPHMET] geomorph problem with morphologika file

[Adams, Dean [EEOBS]]

Jo,

There were two issues with the file.  First, you had single curly quotes in 
front of the specimen names. These should be simple ascii character quotes 
instead.

Second, you had missing data in one of the specimens. For downstream analyses 
in geomorph that is fine, but at present the read.morphologika function does 
not handle these.  The immediate solution is to replace these with -, then 
read the file, and then replace them back to ‘NA’ within R.  You can then 
estimate the missing landmark locations using the ‘estimate.missing’ function 
in geomorph, and continue with your other analyses.

Note that in the next version of geomorph we will add the ability to read 
missing data directly from morphologika files, as is possible when reading TPS 
files. Thank you for bringing this oversight to our attention!

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Jo Gooding [mailto:jg1753.2...@my.bristol.ac.uk]
Sent: Monday, June 8, 2015 1:39 PM
To: morphmet@morphometrics.org
Subject: [MORPHMET] geomorph problem with morphologika file

Dear all,

I'm trying to read a morphologika formatted file into geomorph, but getting the 
following error message:

 read.morphologika(combodatatest.txt)
Error in dimnames(coords)[[3]] - names :
  length of 'dimnames' [3] not equal to array extent

I'm not sure how I've formatted the .txt file incorrectly as it looks in 
keeping with the geomorph manual example (my file is attached). Any suggestions 
would be gratefully received!

Many thanks

Jo

--
Joanna Gooding
MSc Student

Palaeobiology and Biodiversity Research Group
School of Earth Sciences, University of Bristol
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RE: [MORPHMET] Analysis of semilandmark data

[Adams, Dean [EEOBS]]

Nico,

From the image you attached, things seem to be coded properly. In your case, 
the resulting slider file should be a 6 X 3 matrix:

1 2 3
2 3 4
3 4 5
4 5 6
5 6 7
6 7 8

As for analyses, one would first perform a generalized Procrustes analysis, 
including the slider information. Inputs would be the set of xy coordinates for 
all landmarks and semilandmarks, as well as the matrix above designating which 
landmarks are sliders and in what tangent directions they will slide. The 
analysis may be accomplished in geomorph, TPSRelw, or the Evan toolkit.  From 
there, the aligned Procrustes residuals may be utilized as a set of shape 
variables for your group comparisons.

As for tutorials, TPSRelw has an extensive help file, as does EVAN.  Geomorph 
has a user guide that includes tutorial information, and is available at: 
www.geomorph.nethttp://www.geomorph.net.

Hope this helps,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Nico Posnien [mailto:nico.posn...@gmail.com]
Sent: Wednesday, May 6, 2015 6:29 AM
To: morphmet@morphometrics.org
Subject: [MORPHMET] Analysis of semilandmark data

Dear all,

I have a question concerning the analysis of landmark and semilandmark data in 
fly heads. So far I placed landmarks on 2D images. Some of these actually 
represent semilandmarks. I used tps Util (Make Sliders File; see attched 
picture) to generate a sliders file. In the attached picture, 1 and 8 are 
landmarks and 2-7 in between them are supposed to be semilandmarks.

My first question is: Is this the right way to transform landmarks into 
semilandmarks?

My second question is: What is the best (most accepted, straight-forward) way 
to further analyze this data? I am mainly interested in shape differences 
between different groups (2 species + hybrids, males and females). Which 
programs should be used? I saw that geomorph can handle this kind of data. Any 
UI software? Are there any suggestions for good tutorials?

Thanks a lot for any recommendation and help!

Cheers,
Nico
--
Nico Posnien
Georg-August-University Göttingen
Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology
Department of Developmental Biology
Ernst-Caspari-Haus (GZMB)
Justus-von-Liebig-Weg 11
37077 Göttingen
Germany

Phone: +49 (0) 55139 20817
E-mail: npos...@gwdg.demailto:npos...@gwdg.de
web: http://www.evolution.uni-goettingen.de/posnienlab/index.html
web: http://www.uni-goettingen.de/en/44993.html


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[MORPHMET] RE: Testing for sexual dimorphism

[Adams, Dean [EEOBS]]

Miranda,
The required inputs for gpagen do not change based on how you have organized 
the data or how you collected it. From your description, it seems you have 
incorrectly specified the input components.
First, the slide= option is for one to specify a ‘sliders’ matrix, in the style 
used in TPSRelw (a m X 3 matrix specifying which landmarks are semilandmarks 
and in which directions they should be slid). From your email you seem to have 
this in a csv file, so read that into R and use this matrix.
Second, the 3D array of coordinates should contain the xy (or xyz) coordinates 
of both the fixed points as well as the semilandmarks. They do not get input 
separately, but in 1 matrix. In your case, since you seem to have the fixed 
landmarks in one matrix and the semilandmarks in another, you will need to 
concatenate these prior to performing gpagen.
Best,
Dean
Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: Karban, Miranda E [mailto:miranda-utzin...@uiowa.edu]
Sent: Wednesday, April 29, 2015 10:41 AM
To: morphmet@morphometrics.org
Subject: [MORPHMET] RE: Testing for sexual dimorphism

Dear Morphometricians,
I am attempting to upload 2D landmark and semi-landmark data into R (geomorph 
package), and I was wondering if anyone could give me advice. In past analyses, 
I have uploaded both a landmark tps file, which contains all of the landmark 
and semi-landmark data combined (measured using tpsDig2), along with a sliders 
csv file (made using tpsUtil), which indicates which points should be treated 
as semi-landmarks. I am hoping to start using the draw background curves 
(yellow pencil) tool in tpsDig2, which puts the curve information directly into 
the landmark tps file. Does anyone know whether or not I need to indicate 
within R that these curves exist? And if so, how?

I've tried using the following functon:
 slide - as.matrix(read.tps(semilandmarks.tps))

but when I try to use the gpagen function:
 procrustes - gpagen(landmarks, curves=slide, PrinAxes = FALSE)

the program crashes.

Thank you in advance for any help you can provide.

Best,
Miranda Karban

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[MORPHMET] geomorph 2.1.4 released

[Adams, Dean [EEOBS]]

Morphometrics colleagues,

Version 2.1.4 of geomorph has been released. This may be obtained via the CRAN 
R repository, or by installing from our GitHub repository. This version has 
underlying source code that is now compatible with changes in R 3.1.3 use of 
base functions for linear models. As a result, syntax for several functions 
that include covariates have been updated to require formula input (if they 
didn't already).  These functions include plotAllometry, advanced.procD.lm, 
pairwiseD.test, and pairwise.slope.test.  Additionally, a bug in readland.tps 
has been fixed as have several other minor issues. As usual, these changes are 
highlighted in the News file that accompanies the program.

Best,

Dean and the geomorph team


Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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[MORPHMET] New paper: phylogenetic comparative analysis of shape; different permutation methods provide contrasting results

[Adams, Dean [EEOBS]]

Colleagues,

I want to alert your attention to a new article on-line at Evolution (Adams and 
Collyer 2015: doi:10./evo.12596). In it we compare two approaches for 
performing phylogenetic regression of high-dimensional shape data (one based on 
PICs and another based on PGLS). We show that although the two methods produce 
the same test statistics, the permutation procedures commonly employed with the 
methods can produce largely divergent statistical results.  We demonstrate that 
randomization tests using PICs have unacceptably high type I error rates, and 
can lead to incorrect statistical and biological inferences. We then discuss 
the statistical reasons for this, explain why the permutation method 
implemented in the PGLS-based procedure leads to correct statistical and 
biological inferences, and explain how the permutation procedure for PICs can 
be adjusted to provide appropriate results.

Best,

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

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RE: [MORPHMET] Error with read.ply in geomorph

[Adams, Dean [EEOBS]]

David,

Geomorph only reads ASCII ply files (see man pages).  You could use MeshLab or 
some other reader to convert to ASCII ply first. Then it should be fine.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: katz.w...@gmail.com [mailto:katz.w...@gmail.com] On Behalf Of David Katz
Sent: Sunday, December 14, 2014 6:53 PM
To: morphmet@morphometrics.org
Subject: [MORPHMET] Error with read.ply in geomorph

Hello,

When I try to read a 3D surface file (ply ascii format) into geomorph, I get 
the following error:

Error in dim(points) - c(ncolpts, npoints) :
  dims [product 6426920] do not match the length of object [5784228]

For what it's worth, length of object is exactly 90% of dims.

Does anyone have an idea where this problem is coming from and how to fix it? 
Note that the model opens without difficulty as a binary ply in Geomagic.

Thanks.

David Katz

--
David Katz
Doctoral Candidate
Department of Anthropology--Evolutionary Wing
University of California, Davis
Young Hall 204
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RE: [MORPHMET] Re: gpagen in geomorph flipping axes?

[Adams, Dean [EEOBS]]

Tim,

You might also make sure you have the very latest version of geomorph from CRAN 
(version 2.1.2). In the previous version with PrinAxes the ‘flipping’ sometimes 
occurred. That should be resolved in vsn 2.1.2.

Dean

Dr. Dean C. Adams
Professor
Department of Ecology, Evolution, and Organismal Biology
   Department of Statistics
Iowa State University
www.public.iastate.edu/~dcadams/http://www.public.iastate.edu/~dcadams/
phone: 515-294-3834

From: timastrop [mailto:timast...@gmail.com]
Sent: Wednesday, December 10, 2014 2:50 PM
To: morphmet@morphometrics.org
Subject: [MORPHMET] Re: gpagen in geomorph flipping axes?

Thanks everyone for the helpful insight!

Turns out that by including PrinAxes=False in the gpagen command the original 
orientation of the specimen is maintained (thanks to the ever helpful Emma 
Sherratt for pointing this out!)

On Wednesday, December 10, 2014 7:35:59 PM UTC, timastrop wrote:
Hello all,

Does anyone have any experience with 2D landmarks in the geomorph package? When 
using the gpagen function my specimens are projected at different rotations 
depending on some unknown factor. Can anyone help?

Tim

Tim Astrop PhDhttp://timastrop.wordpress.com/
Ammonoid Palaeobiology Labhttp://aplbath.wordpress.com/
Department of Biology  Biochemistry
University of Bath
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