Yes, you can do that and the help page for that function has examples to do 


> On Nov 5, 2018, at 5:26 AM, Igor Talijančić <talijancic.i...@gmail.com> wrote:
> 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$coords?
> Thank you for your given time and consideration.
> Sincerely,
> Igor
> Dana srijeda, 25. srpnja 2018. u 14:42:41 UTC+2, korisnik javiersantos3 
> napisao je:
> Hello Carmelo and Mike,
> Thanks for the quick response! I see things now clearer, especially with the 
> examples you have both provided. Sometimes one gets disoriented in the 
> abstractness of shape space and coding ;-P  Thanks again!
> Best wishes,
> Javier
> From: Mike Collyer <mlco...@gmail.com <>>
> Sent: Wednesday, July 25, 2018 2:29:38 PM
> To: Javier Santos
> Cc: Morphomet Mailing List
> Subject: Re: Conceptual clarification of plotting shape deformation grids in 
> geomorph
> Javier,
> First your plotting question.  The plot.trajectory.analysis function is an S3 
> generic plot function, which means you can modify the plot as you like.  You 
> do this easiest with the points function.  Here is an example, using the help 
> page example, which hopefully makes sense for you:
> data(plethodon) 
> Y.gpa <- gpagen(plethodon$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 <javier...@hotmail.com <>> 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, the points corresponding to each 
>> individual are grey for all species. How can I color these points by species 
>> group without exporting the data?
>> I would also like to plot the shape deformation that corresponds to each PC 
>> axis like the function plotTangentSpace() does. How can that be coded from 
>> the output of the trajectory.analysis()?
>> And lastly, how do you code, for example, when the shape deformation you 
>> want to plot corresponds to the $pred.val of the regression in the 
>> procD.allometry() function output [in contrast to PC values]?
>> Any help on how to approach coding these circumstances or any explicative 
>> literature on the topic would be greatly appreciated.
>> Best wishes,
>> Javier

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