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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