Dear colleagues,
thank you so much for your guidelines regarding proper code syntax of the
shape visualization of trajectory analysis!
Thank you so much!
Sincerely,
Igor
Dana srijeda, 7. studenoga 2018. u 14:53:47 UTC+1, korisnik Dean Adams
napisao je:
>
> 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 <talijan...@gmail.com <javascript:>>
> *Sent:* Monday, November 5, 2018 4:26 AM
> *To:* MORPHMET <morp...@morphometrics.org <javascript:>>
> *Cc:* mlco...@gmail.com <javascript:>
> *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$coords?
>
>
>
> Thank you for your given time and consideration.
>
>
>
> Sincerely,
>
> Igor
>
>
> Dana srijeda, 25. srpnja 2018. u 14:42:41 UTC+2, korisnik javiersantos3
> napisao je:
>
> Hello Carmelo and Mike,
>
>
>
> Thanks for the quick response! I see things now clearer, especially with
> the examples you have both provided. Sometimes one gets disoriented in the
> abstractness of shape space and coding ;-P Thanks again!
>
>
>
>
>
> Best wishes,
>
> Javier
>
>
>
>
> ------------------------------
>
> *From:* Mike Collyer <mlco...@gmail.com>
> *Sent:* Wednesday, July 25, 2018 2:29:38 PM
> *To:* Javier Santos
> *Cc:* Morphomet Mailing List
> *Subject:* Re: Conceptual clarification of plotting shape deformation
> grids in geomorph
>
>
>
> Javier,
>
>
>
> First your plotting question. The plot.trajectory.analysis function is an
> S3 generic plot function, which means you can modify the plot as you like.
> You do this easiest with the points function. Here is an example, using
> the help page example, which hopefully makes sense for you:
>
>
>
> data(plethodon)
>
> Y.gpa <- gpagen(plethodon$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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