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/<http://www.public.iastate.edu/~dcadams/>
phone: 515-294-3834
From: Igor Talijančić
Sent: Monday, November 5, 2018 4:26 AM
To: MORPHMET
Cc: mlcoll...@gmail.com
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$coords?
Thank you for your given time and consideration.
Sincerely,
Igor
Dana srijeda, 25. srpnja 2018. u 14:42:41 UTC+2, korisnik javiersantos3 napisao
je:
Hello Carmelo and Mike,
Thanks for the quick response! I see things now clearer, especially with the
examples you have both provided. Sometimes one gets disoriented in the
abstractness of shape space and coding ;-P Thanks again!
Best wishes,
Javier
From: Mike Collyer >
Sent: Wednesday, July 25, 2018 2:29:38 PM
To: Javier Santos
Cc: Morphomet Mailing List
Subject: Re: Conceptual clarification of plotting shape deformation grids in
geomorph
Javier,
First your plotting question. The plot.trajectory.analysis function is an S3
generic plot function, which means you can modify the plot as you like. You do
this easiest with the points function. Here is an example, using the help page
example, which hopefully makes sense for you:
data(plethodon)
Y.gpa <- gpagen(plethodon$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 >
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
