Arothron (version 2.0.5)

dta: dta

Description

This function applyes the Digital Alignment Tool (DTA) on a disarticulated model using a reference sample

Usage

dta(
  RM_sample,
  mod_1,
  mod_2,
  pairs_1,
  pairs_2,
  DM_mesh_1,
  DM_mesh_2,
  DM_set_1,
  DM_set_2,
  method = c("euclidean")
)

Value

AM_mesh mesh3d: mesh of the aligned model

AM_set matrix: landmark configuration of the aligned model

AM_id character: name of the item of the reference sample resulted as best DTA

AM_SF_1 numeric: scale factor used to scale the reference set (first module)

AM_SF_2 numeric: scale factor used to scale the reference set (second module)

distance numeric: distance between the landmark configuration of the aligned and the reference model

tot_proc numeric vector: procrustes distances between aligned and reference models (all DTAs)

tot_eucl numeric vector: euclidean distances between aligned and reference models (all DTAs)

setarray 3D array: landmark configurations of the disarticulated model aligned on each item of the reference sample

Arguments

RM_sample

3D array: 3D landmark configurations of the reference sample

mod_1

numeric vector: vector containing the position of which landmarks belong to the first module

mod_2

numeric vector: vector containing the position of which landmarks belong to the second module

pairs_1

matrix: a X x 2 matrix containing the indices of right and left landmarks of the first module

pairs_2

matrix: a X x 2 matrix containing the indices of right and left landmarks of the second module

DM_mesh_1

mesh3d: mesh of the disarticulated model (first module)

DM_mesh_2

mesh3d: mesh of the disarticulated model (second module)

DM_set_1

matrix: 3D landmark set of the first module acquired on the disarticulated model

DM_set_2

matrix: 3D landmark set of the second module acquired on the disarticulated model

method

character: specify method to be used to individuate the best DTA ("euclidean" or "procrustes")

Author

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

References

Profico, A., Buzi, C., Davis, C., Melchionna, M., Veneziano, A., Raia, P., & Manzi, G. (2019). A new tool for digital alignment in Virtual Anthropology. The Anatomical Record, 302(7), 1104-1115.

Examples

## Load and plot the disarticulated model of the Homo sapiens case study
library(compositions)
library(rgl)
data(DM_base_sur)
data(DM_face_sur)
open3d()
wire3d(DM_base_sur,col="white")
wire3d(DM_face_sur,col="white")
## Load the landmark configurations associated to the DM 
data(DM_set)
## Load the reference sample 
data(RMs_sets)
## Define the landmarks belonging to the first and second module
mod_1<-c(1:17) #cranial base
mod_2<-c(18:32) #facial complex
## Define the paired landmarks for each module (optional symmetrization process)
pairs_1<-cbind(c(4,6,8,10,12,14,16),c(5,7,9,11,13,15,17))
pairs_2<-cbind(c(23,25,27,29,31),c(24,26,28,30,32))
## Run DTA
ex.dta<-dta(RM_sample=RMs_sets, mod_1=mod_1, mod_2=mod_2, pairs_1=pairs_1, pairs_2=pairs_2,
DM_mesh_1=DM_base_sur,DM_mesh_2=DM_face_sur, DM_set_1= DM_set[mod_1,], DM_set_2=DM_set[mod_2,])
## Print the name of the best RM 
ex.dta$AM_id
## Save the mesh and the landmark set of the AM
AM_mesh<-ex.dta$AM_mesh
AM_set<-ex.dta$AM_set
## Plot the aligned 3D model
library(compositions)
library(rgl)
open3d()
wire3d(AM_mesh,col="white")
plot3D(AM_set,bbox=FALSE,add=TRUE)