eometric Morphometric Methods and Virtual Anthropology Tools

Description

Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).

Author(s)

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

example dataset

Description

2D image of the Altamura man fossil

Usage

data(Altapic)

Author(s)

Antonio Profico

CScorreffect Plot showing the correlation in the shape space between original and combined dataset omitting or including the normalization factors calculated with Arothron and MLECScorrection

Description

CScorreffect Plot showing the correlation in the shape space between original and combined dataset omitting or including the normalization factors calculated with Arothron and MLECScorrection

Usage

CScorreffect(
  array1,
  array2,
  nPCs = c(1:3),
  from = 0.02,
  to = 0.9,
  length.out = 100
)

Arguments

Value

PCscores PCscores matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation

PCs PCs matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation

corr mean correlation between original and combined dataset

CSratios normalization factor calculated by using the maximum likelihood estimation

Author(s)

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

Examples

## Not run: 
# Femora case study
data(femsets)
all_pois<-matrix(1:(200*61),nrow=61,ncol=200,byrow = FALSE)
set_ext_100<-femsets[all_pois[,1:100],,]

set_int_100<-femsets[all_pois[,101:200],,]
set_int_50<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,2),]),,]
set_int_20<-set_int_100[c(matrix(1:6100,ncol=61)[seq(1,100,5),]),,]
set.seed(123)
sel<-sample(1:100,10)
set_int_10r<-set_int_100[c(matrix(1:6100,ncol=61)[sel,]),,]

CScorreffect(set_ext_100,set_int_50,nPCs=1:3)
CScorreffect(set_ext_100,set_int_20,nPCs=1:3)
CScorreffect(set_ext_100,set_int_10r,nPCs=1:3)

## End(Not run)

example dataset

Description

3D mesh of the first part of the Homo sapiens disarticulated model

Usage

data(DM_base_sur)

Author(s)

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

example dataset

Description

3D mesh of the second part of the Homo sapiens disarticulated model

Usage

data(DM_face_sur)

Author(s)

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

example dataset

Description

Landmark configurations of the two part of the disarticulated model

Usage

data(DM_set)

Author(s)

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

example dataset

Description

List containing five 2D-landmark configurations acquired along five different anatomical views

Usage

data(Lset2D_list)

Author(s)

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

example dataset

Description

Array containing a cranial 3D-landmark configuration acquired on a Primate sample

Usage

data(Lset3D_array)

Author(s)

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

example dataset

Description

Landmark configurations of the manual alignments

Usage

data(MAs_sets)

Author(s)

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

MLECScorrection Maximum Likelihood Estimation of the normalization factor to be applied to optimize the correlation between two landmark configurations to be combined by using twodviews and

Description

MLECScorrection Maximum Likelihood Estimation of the normalization factor to be applied to optimize the correlation between two landmark configurations to be combined by using twodviews and

Usage

MLECScorrection(array1, array2, scale = TRUE, nPCs = 1:5)

Arguments

Value

PCscores PCscores matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation

PCs PCs matrix of the combined dataset applying the normalization factor calculated by using the maximum likelihood estimation

corr mean correlation between original and combined dataset

CSratios normalization factor calculated by using the maximum likelihood estimation

Author(s)

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

PCscoresCorr Perform a correlation test between two matrices of PCscores

Description

PCscoresCorr Perform a correlation test between two matrices of PCscores

Usage

PCscoresCorr(matrix1, matrix2, nPCs = 1:5)

Arguments

Value

corr the correlation values associated to each pair of PC scores

p.values p-values associated to the correlation test

Author(s)

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

example dataset

Description

Array containing the landmark coordinates of the reference sample for Digital Alignment Tool example

Usage

data(RMs_sets)

Author(s)

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

example dataset

Description

Mesh of the Saccopastore 1 Neanderthal skull

Usage

data(SCP1.mesh)

Author(s)

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

example dataset

Description

Landmark configuration associated to the starting model

Usage

data(SM_set)

Author(s)

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

aro.clo.points

Description

Find the closest matches between a reference (2D or 3D matrix) and a target matrix (2D/3D) or mesh returning row indices and distances

Usage

aro.clo.points(target, reference)

Arguments

Value

position numeric: a vector of the row indices

distances numeric: a vector of the coordinates distances

Author(s)

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

Examples

#load an example: mesh, and L set
data(yoda_sur)
data(yoda_set)
sur<-yoda_sur
set<-yoda_set
ver_pos<-aro.clo.points(target=sur,reference=set) 

arraytolist

Description

converts an array in a list storing each element of the third dimension of the array (specimen) as element of the list

Usage

arraytolist(array)

Arguments

Value

a list containing the landmark configurations stored as separated elements

Author(s)

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

bary.mesh

Description

This function calculates the barycenter of a matrix or a 3D mesh

Usage

bary.mesh(mesh)

Arguments

Value

barycenter numeric: x,y,z coordinates of the barycenter of the mesh

Author(s)

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

Examples

#load an example: mesh, and L set
data(SCP1.mesh)
sur<-SCP1.mesh
bary<-bary.mesh(mesh=sur)

compare_check.set

Description

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

Usage

compare_check.set(RM_set_1, RM_set_2, DM_set_1, DM_set_2, DM_mesh_1, DM_mesh_2)

Arguments

Value

SF1 numeric: scale factor used to scale the reference set (first module)

SF2 numeric: scale factor used to scale the reference set (second module)

RM_set_1_sc matrix: scaled 3D reference set (first module)

RM_set_2_sc matrix: scaled 3D reference set (second module)

AM_model list: output of the Morpho::rotmesh.onto function

dist_from_mesh numeric: mesh distance between the aligned model and the scaled reference set

eucl_dist_1 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (first module)

eucl_dist_2 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (second module)

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

procr_dist_1 numeric: procrustes distance between the landmark configuration of the disarticulated and reference model (first module)

procr_dist_2 numeric: procrustes distance between the landmark configuration of the disarticulated and reference model (second module)

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

single_l_1 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (first module)

single_l_2 numeric: euclidean distance between the landmark configuration of the disarticulated and reference model (second module)

Author(s)

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

dec.curve

Description

This function computes the order of points on a open 3D curve and finds intermediate points

Usage

dec.curve(mat_input, mag, plot = TRUE)

Arguments

Value

matt numeric: a kx3 matrix with points coordinates

Author(s)

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

Examples

## Not run: 
## Create and plot a 3D curve
require(compositions)
require(rgl)
curve_3D<-cbind(1:10,seq(1,5,length=10),rnorm(10,sd = 0.2))
plot3D(curve_3D,bbox=FALSE)
close3d()
## Create and plot the new 3D curve (with intermediate points)
dec_curve_3D<-dec.curve(curve_3D, 2, plot = TRUE)

## End(Not run)

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")
)

Arguments

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

Author(s)

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)

example dataset

Description

POVs defined inside the endocranial cavity

Usage

data(endo_set)

Author(s)

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

endomaker

Description

Build endocast from a skull 3D mesh

Usage

endomaker(
  mesh = NULL,
  path_in = NULL,
  param1_endo = 1,
  npovs = 50,
  volume = TRUE,
  alpha_vol = 100,
  nVoxels = 1e+05,
  decmesh = 20000,
  alpha_ext = 30,
  ncells = 50000,
  npovs_calse = 50,
  param1_calse = 2,
  param1_ast = 1.3,
  decendo = 20000,
  scalendo = 0.5,
  alpha_end = 100,
  mpovdist = 10,
  plot = FALSE,
  colmesh = "orange",
  save = FALSE,
  outpath = tempdir(),
  num.cores = NULL
)

Arguments

Value

endocast mesh3d: mesh of the endocast

volume numeric: volume of the endocast expressed in cc

Author(s)

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

References

Profico, A., Buzi, C., Melchionna, M., Veneziano, A., & Raia, P. (2020). Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. American Journal of Physical Anthropology.

Examples

## Not run: 
library(rgl)
data(human_skull)
sapendo<-endomaker(human_skull,param1_endo = 1.0,decmesh = 20000, num.cores=NULL)
open3d()
wire3d(sapendo$endocast,col="violet")
ecv<-sapendo$volume

## End(Not run)

endomaker_dir

Description

Build library of endocasts from skull 3D meshes

Usage

endomaker_dir(
  dir_path,
  param1_endo = 1.5,
  npovs = 50,
  volume = TRUE,
  alpha_vol = 50,
  nVoxels = 1e+05,
  decmesh = 20000,
  alpha_ext = 30,
  ncells = 50000,
  npovs_calse = 50,
  param1_calse = 3,
  param1_ast = 1.3,
  decendo = 20000,
  scalendo = 0.5,
  alpha_end = 100,
  mpovdist = 10,
  plotall = FALSE,
  colmesh = "orange",
  save = FALSE,
  outpath = tempdir(),
  num.cores = NULL
)

Arguments

Value

endocasts mesh3d: list of meshes of the extracted endocasts

volumes numeric: volumes of the endocasts expressed in cc

Author(s)

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

References

Profico, A., Buzi, C., Melchionna, M., Veneziano, A., & Raia, P. (2020). Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. American Journal of Physical Anthropology.

export_amira

Description

This function exports a list of 3D landmark set in separate files (format landmarkAscii)

Usage

export_amira(lista, path)

Arguments

Author(s)

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

Examples

x<-c(1:20)
y<-seq(1,3,length=20)
z<-rnorm(20,0.01)
vertices<-cbind(x,y,z)
set<-list(vertices)
example<-export_amira(set,path=tempdir()) 

export_amira.path

Description

Convert and save a 3D matrix into a AmiraMesh ASCII Lineset (.am) object

Usage

export_amira.path(
  vertices,
  filename,
  Lines = c(1:(dim(vertices)[1] - 1) - 1, -1),
  path
)

Arguments

Author(s)

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

Examples

x<-c(1:20)
y<-seq(1,3,length=20)
z<-rnorm(20,0.01)
vertices<-cbind(x,y,z)
export_amira.path(vertices=vertices,filename="example_line",path=tempdir())

ext.int.mesh

Description

This function finds the vertices visible from a set of points of view

Usage

ext.int.mesh(
  mesh,
  views = 20,
  dist.sphere = 3,
  param1 = 2.5,
  param2 = 10,
  default = TRUE,
  import_pov,
  matrix_pov,
  expand = 1,
  scale.factor,
  method = "ast3d",
  start.points = 250,
  num.cores = NULL
)

Arguments

Value

position numeric: a vector with vertex number nearest the landmark set

Author(s)

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

References

Profico, A., Schlager, S., Valoriani, V., Buzi, C., Melchionna, M., Veneziano, A., ... & Manzi, G. (2018). Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools. American Journal of Physical Anthropology, 166(4), 979-986.

ext.mesh.rai

Description

This function returns a 3D mesh with colours based on the vertices visibile from each point of view

Usage

ext.mesh.rai(scans, mesh)

Arguments

Author(s)

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

example dataset

Description

3D semilandmark configurations of 21 human femora

Usage

data(femsets)

Author(s)

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

grid_pov

Description

This function creates a grid for an interactive way to define the set of the points of view

Usage

grid_pov(mesh, expand = 1)

Arguments

Value

matrice matrix: matrix with the x,y,z coordinates of the points of view

Author(s)

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

example dataset

Description

3D mesh of a human skull

Usage

data(human_skull)

Author(s)

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

image2palettes

Description

Create palettes from an image

Usage

image2palettes(
  array,
  resize = 4,
  unique = FALSE,
  scale = F,
  k = 3,
  lcols = 7,
  plsaxis = 1,
  cex = 5,
  cext = 0.5
)

Arguments

Value

paletteslist list: color palettes arranged in a list

Author(s)

Antonio Profico

Examples

## Not run: 
require(jpeg)
require(Morpho)
data("Altapic")
image2palettes(Altapic,resize=1,unique=T,scale=T,k=3,lcols=5,plsaxis=1,cext=0.5)

## End(Not run)

example dataset

Description

3D mesh of a decidous Neanderthal tooth

Usage

data(krd1_tooth)

Author(s)

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

landmark_frm2amira

Description

This function converts the .frm files, from Evan Toolbox, stored in a folder into the format landmarkAscii

Usage

landmark_frm2amira(path_folder_frm, path_amira_folder)

Arguments

Author(s)

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

listtoarray convert a list into an array

Description

listtoarray convert a list into an array

Usage

listtoarray(mylist)

Arguments

Value

a kx3xn array with landmark coordinates

Author(s)

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

localmeshdiff Calculate and Visualize local differences between two meshes

Description

localmeshdiff Calculate and Visualize local differences between two meshes

Usage

localmeshdiff(
  mesh1,
  mesh2,
  ploton = 1,
  diffarea = ((area_shape1 - area_shape2)/area_shape2) * 100,
  paltot = rainbow(200),
  from = NULL,
  to = NULL,
  n.int = 200,
  out.rem = TRUE,
  fact = 1.5,
  visual = 1,
  scale01 = TRUE,
  colwire = "pink"
)

Arguments

Value

vect numeric vector containing local differeces in area between the reference and the target mesh

Author(s)

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

References

Melchionna, M., Profico, A., Castiglione, S., Sansalone, G., Serio, C., Mondanaro, A., ... & Manzi, G. (2020). From smart apes to human brain boxes. A uniquely derived brain shape in late hominins clade. Frontiers in Earth Science, 8, 273.

Examples

## Not run: 
library(Arothron)
library(rgl)
data("primendoR")
neaset<-primendoR$sets[,,11]
sapset<-primendoR$sets[,,14]
#defining a mesh for the neanderthal right hemisphere
neasur<-list("vb"=t(cbind(neaset,1)),"it"=primendoR$sur$it)
class(neasur)<-"mesh3d"
#defining a mesh for the modern human right hemisphere
sapsur<-list("vb"=t(cbind(sapset,1)),"it"=primendoR$sur$it)
class(neasur)<-"mesh3d"
layout3d(t(c(1,2)),sharedMouse = TRUE)
localmeshdiff(sapsur,neasur,1,scale01 = TRUE,
paltot=c("darkred","red","orange","white","lightblue","blue","darkblue"))
next3d()
localmeshdiff(neasur,sapsur,1,scale01 = TRUE,
paltot=c("darkred","red","orange","white","lightblue","blue","darkblue"))

## End(Not run)

example dataset

Description

3D mesh of a human malleus

Usage

data(malleus_bone)

Author(s)

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

noise.mesh

Description

This function adds noise to a mesh

Usage

noise.mesh(mesh, noise = 0.025, seed = 123)

Arguments

Value

mesh_n a 3D model of class "mesh3d" with noise

Author(s)

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

Examples

#load mesh
library(compositions)
library(rgl)
data("SCP1.mesh")
mesh<-SCP1.mesh
#add noise
noised<-noise.mesh(mesh,noise=0.05)
#plot original and mesh with noise added
open3d()
shade3d(mesh,col=3)
shade3d(noised,col=2,add=TRUE)

out.inn.mesh

Description

This function separates a 3D mesh subjected to the ext.int.mesh into two 3D models: the visible mesh and the not visible one

Usage

out.inn.mesh(scans, mesh, plot = TRUE)

Arguments

Author(s)

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

Examples

## Not run: 
#CA-LSE tool on Neanderthal tooth
#load a mesh
data(krd1_tooth)
library(rgl)
library(Rvcg)
library(compositions)
ca_lse_krd1<-ext.int.mesh(mesh= krd1_tooth, views=50, param1=3, default=TRUE, 
import_pov = NULL,expand=1, scale.factor=1,num.cores = NULL)
vis_inv_krd1<-out.inn.mesh(ca_lse_krd1, krd1_tooth, plot=TRUE)
inv_mesh<-vcgIsolated(vis_inv_krd1$invisible)
open3d()
shade3d(inv_mesh,col=2)
open3d()
shade3d(vis_inv_krd1$visible, col=3)
#CA-LSE tool on human malleus
#load a mesh
data(malleus_bone)
ca_lse_malleus<-ext.int.mesh(mesh= malleus_bone, views=50, param1=3, 
default=TRUE, import_pov = NULL, expand=1, scale.factor=1)
vis_inv_malleus<-out.inn.mesh(ca_lse_malleus, malleus_bone, plot=TRUE)
inv_mesh<- vis_inv_malleus$invisible
inv_mesh<-ca_lse_malleus$invisible

#AST-3D tool
#load a mesh
data(human_skull)
data(endo_set)
ast3d_endocast<-ext.int.mesh(mesh=human_skull, views=50, param1=0.6, default=FALSE, 
import_pov = TRUE,expand=1, matrix_pov =endo_set, scale.factor=1,num.cores = NULL)
vis_inv_endo<-out.inn.mesh(ast3d_endocast,human_skull,plot=TRUE)
vis_mesh<-vcgIsolated(vis_inv_endo$visible)
open3d()
shade3d(vis_mesh,col=3)
open3d()
shade3d(vis_inv_endo$invisible, col=2)

## End(Not run)

patches_frm2amira

Description

This function converts the .frm files, from Evan Toolbox, stored in a folder into the format landmarkAscii (semilandmark patches)

Usage

patches_frm2amira(path_folder_frm, path_amira_folder)

Arguments

Author(s)

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

permutangle

Description

Create palettes from an image

Usage

permutangle(
  mat,
  var,
  group1,
  group2,
  scale = FALSE,
  iter = 100,
  cex1 = range01(var[group1] + 1),
  cex2 = range01(var[group2] + 1),
  cex3 = 0.7,
  cex4 = 1.2,
  labels = c("stgr1", "stgr2", "endgr1", "endgr2"),
  pch1 = 19,
  pch2 = 19,
  pch3 = 19,
  col1 = "red",
  col2 = "blue"
)

Arguments

Value

angle list: color palettes arranged in a list

permangles list: color palettes arranged in a list

angle list: color palettes arranged in a list

iterangles list: color palettes arranged in a list

p-value list: color palettes arranged in a list

PCA_angle list: color palettes arranged in a list

PCA_interangles list: color palettes arranged in a list

PCA_p-value list: color palettes arranged in a list

Author(s)

Antonio Profico

Examples

## Not run: 
require(shapes)
require(Morpho)
data("gorf.dat")
data("gorm.dat")
Array<-bindArr(gorf.dat,gorm.dat,along=3)
CS<-apply(Array,3,cSize)
Sex<-c(rep("F",dim(gorf.dat)[3]),rep("M",dim(gorm.dat)[3]))

#Shape and size space
AllTrajFB<-permutangle(procSym(Array,scale=FALSE,CSinit = FALSE)$PCscores,
var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50)
hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90))
abline(v=AllTrajFB$angle,lwd=2,col="red")
hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90))
abline(v=AllTrajFB$PCA_angle,lwd=2,col="red")

#Shape space
AllTrajFB<-permutangle(procSym(Array)$PCscores,
var=CS,group1=which(Sex=="F"),group2=which(Sex=="M"),scale=FALSE,iter=50)
hist(AllTrajFB$iterangles,breaks = 100,xlim=c(0,90))
abline(v=AllTrajFB$angle,lwd=2,col="red")
hist(AllTrajFB$PCA_iterangles,breaks = 100,xlim=c(0,90))
abline(v=AllTrajFB$PCA_angle,lwd=2,col="red")

## End(Not run)

pov_selecter

Description

Internal function to define the points of view

Usage

pov_selecter(mesh, grid, start.points = 250, method = "ast3d")

Arguments

Value

selection numeric: positioning vector of the selected points of the grid

Author(s)

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

example dataset

Description

right brain hemisphere of 19 primate species

Usage

data(primendoR)

Author(s)

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

read.amira.dir

Description

This function reads and stores in an array the coordinated allocated in a folder in separate files (format landmarkAscii)

Usage

read.amira.dir(path.dir, nland)

Arguments

Value

array.set numeric: a kx3xn array with landmark coordinates

Author(s)

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

read.amira.set

Description

This function converts a landmarkAscii file set in a kx3x1 array

Usage

read.amira.set(name.file, nland)

Arguments

Value

array.set numeric: a kx3x1 array with landmark coordinates

Author(s)

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

read.path.amira

Description

This function extracts and orders the coordinate matrix from a surface path file from Amira

Usage

read.path.amira(path.name)

Arguments

Value

data numeric: a kxd matrix with xyz coordinates

Author(s)

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

repmat

Description

This function repeats copies of a matrix

Usage

repmat(X, m, n)

Arguments

Value

matrice: repeated matrix

Author(s)

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

example dataset

Description

POVs sampled inside the maxillary sinus cavity

Usage

data(sinus_set)

Author(s)

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

spherical.flipping

Description

Internal spherical flippping function

Usage

spherical.flipping(C, mesh, param1, param2)

Arguments

Author(s)

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

References

Profico, A., Schlager, S., Valoriani, V., Buzi, C., Melchionna, M., Veneziano, A., ... & Manzi, G. (2018). Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools. American Journal of Physical Anthropology, 166(4), 979-986.#' @export

Katz, S., Tal, A., & Basri, R. (2007). Direct visibility of point sets. In ACM SIGGRAPH 2007 papers (pp. 24-es).

trasf.mesh

Description

This function centers a mesh on the barycenter coordinates

Usage

trasf.mesh(mesh, barycenter)

Arguments

Value

mesh a 3D mesh of class "mesh3d"

Author(s)

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

twodvarshape Calculates the shape variation associated to a value of PC scores associated to a specific combined landmark configuration or view

Description

twodvarshape Calculates the shape variation associated to a value of PC scores associated to a specific combined landmark configuration or view

Usage

twodvarshape(twodviews_ob, scores, PC, view)

Arguments

Value

mat matrix of coordinates associated to the called shape variation

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(Arothron)
#load the 2D primate dataset
data("Lset2D_list")
#combine the 2D datasets and PCA
combin2D<-twodviews(Lset2D_list,scale=TRUE,vector=c(1:5))
#calculate the shape variation associated to the negative extreme value of PC1
min_PC1<-twodvarshape(combin2D,min(combin2D$PCscores[,1]),1,5)
plot(min_PC1,asp=1)
#calculate the shape variation associated to the positive extreme value of PC1
max_PC1<-twodvarshape(combin2D,max(combin2D$PCscores[,1]),1,5)
plot(max_PC1,asp=1)

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

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))

volendo

Description

Calculate the volume of a mesh by using a voxel-based method

Usage

volendo(mesh, alpha_vol = 100, ncells = 1e+05)

Arguments

Value

vol numeric: volume of the mesh expressed in cc

Author(s)

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

Examples

## Not run: 
#load the human skull
library(rgl)
data(human_skull)
sapendo<-endomaker(human_skull,param1_endo = 1.0,vol=FALSE, num.cores=NULL)
volsap<-volendo(sapendo$endocast)

## End(Not run)

example dataset

Description

Landmark set on Yoda

Usage

data(yoda_set)

Author(s)

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

example dataset

Description

Mesh of Yoda

Usage

data(yoda_sur)

Author(s)

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