Thank you so much for the quick response Mike! Dana ponedjeljak, 5. studenoga 2018. u 11:38:37 UTC+1, korisnik Mike Collyer napisao je: > > 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 <talijan...@gmail.com > <javascript:>> 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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