Arothron (version 2.0.5)

CScorreffect: 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
)

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

Arguments

array1

array: first set of landmark configuration

array2

array: second set of landmark configuration

nPCs

numeric vector: specify which PC scores will be selected in the correlation test

from

numeric: the lower interval of the normalization factor distribution

to

numeric: the lower interval of the normalization factor distribution

length.out

numeric: number of values ranged between from and to

Author

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

Examples

if (FALSE) {
# 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)
}