Description Usage Arguments Author(s) References Examples
This function perform the Euclidean Parallel transport for allometric investigations using the Linear Shift startegy described in Piras et al (2016). The Linear Shift is performed on MANCOVA predictions and original data are projected on PCA space computed on them.
1 2 |
array |
numeric: an array kxmxn of landmark coordinates |
factor |
character: variable factor that affiliates shapes to group levels |
CSinit |
logical: if TRUE shapes are scaled at unit size (default=TRUE) |
sepure |
logical: if TRUE separate per-group multivariate regression between shape and size are performed on shapes aligned after separate GPAs (default=FALSE) |
polyn |
numeric: default=1 the degree of regression |
perm |
numeric: number of permutations for group non parametric regression |
Paolo Piras
Piras P., Teresi L., Traversetti L., Varano V, Gabriele S., Kotsakis T., Raia P., Puddu P.E., Scalici M. (2016). The conceptual framework of ontogenetic trajectories: Parallel Transport allows the recognition and visualization of pure deformation patterns. Evolution and Development 18: 182-200. doi: 10.1111/ede.12186
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## Not run:
library(Morpho)
library(gdata)
library(rgl)
data(group)
data(my2d)
mysel<-group%in%c("Macroscelides_proboscideus","Petrodromus_tetradactylus","Elephantulus_rozeti","Elephantulus_edwardii")
linksdors<-list(c(1,2),c(37,7),c(12,4),c(27,28),c(25,21),c(38,40),c(9,10),c(2,3),c(3,4),c(1,7),c(1,6),c(3,5),c(6,40),c(5,9),c(40,8),c(8,9),c(1,7),c(7,6),c(3,4),c(4,5),c(39,38),c(38,35),c(35,37),c(37,39),c(35,34),c(34,33),c(33,32),c(32,31),c(31,30),c(30,29),c(29,37),c(37,36),c(36,29),c(28,31),c(28,30),c(13,10),c(10,11),c(11,12),c(12,13),c(13,14),c(14,16),c(16,17),c(17,20),c(20,19),c(19,18),c(18,12),c(18,15),c(15,12),c(21,19),c(21,20),c(24,25),c(25,26),c(26,27),c(27,24),c(26,24),c(24,23),c(23,22),c(22,8),c(8,2))
dors4<-my2d[,,mysel]
factordors4<-drop.levels(group[mysel],reorder=T)
factordors4<-factor(factordors4,levels=unique(factordors4))
adors4<-procSym(dors4,scale=F,pcAlign=F,reflect=F)
mypredictbook<-read.inn(predict(lm(array2mat(adors4$orpdata,105,80)~poly(adors4$size,1,raw=T)*factordors4)),40,2)
procbook<-procSym(mypredictbook,pcAlign=F)
# linear shift in the shape space
plot(procbook$PCscores[,1:2],pch=as.numeric(factordors4),asp=1)
objptau<-ptau6(dors4,factordors4,CSinit=T)
# morphological apprciation is dramatically different!
plotptau5(objptau,linksdors,pch=as.numeric(objptau$factorord),col=rep(1,length(objptau$factorord)),round(max(centroid.size(objptau$arrayord)),digits=0)/2,shiftnegy=2,mag=2,subplotdim=1)
## End(Not run)
## End(Not run)
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