twodviews {Arothron}

twodviews Combine and calculate the PCscores matrix from a list of different landmark configurations to be combined

Description

twodviews Combine and calculate the PCscores matrix from a list of different landmark configurations to be combined

Usage

twodviews(twodlist, scale = TRUE, vector = NULL)

Arguments

twodlist

a list containing the landmark configurations of each anatomical view stored as separated lists

scale

logical: TRUE for shape-space, FALSE for form-space

vector

numeric vector: defines which views are to be used

Value

PCscores PC scores

PCs Pricipal Components (eigenvector matrix)

Variance table of the explained variance by the PCs

size vector containing the Centroid Size of each configuration

mshapes a list containing the mean shape of each landmark configuration

dims number of landmarks of each configuration

dimm dimension (2D or 3D) of each combined landmark configuration

twodlist the list used as input

Author(s)

Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano

References

Profico, A., Piras, P., Buzi, C., Del Bove, A., Melchionna, M., Senczuk, G., ... & Manzi, G. (2019). Seeing the wood through the trees. Combining shape information from different landmark configurations. Hystrix, 157-165.

Examples

library(Morpho)
#load the 2D primate dataset
data("Lset2D_list")
length(Lset2D_list)
#combine the 2D datasets and PCA
combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5))
combin2D$size
#plot of the first two Principal Components
plot(combin2D$PCscores)
text(combin2D$PCscores,labels=rownames(combin2D$PCscores))
#load the 3D primate dataset
data("Lset3D_array")
#GPA and PCA
GPA_3D<-procSym(Lset3D_array)
#plot of the first two Principal Components
plot(GPA_3D$PCscores)
text(GPA_3D$PCscores,labels=rownames(GPA_3D$PCscores))