Igor,
Yes, you can do that and the help page for that function has examples to do
that.
Mike
> 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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