xyplotPCArchetypes {Anthropometry}

PC scores for archetypes

Description

This function is a small modification of the generic xyplot function of the archetypes R package. It shows the scores for the principal components of all individuals jointly with the scores for the computed archetypes. This function is used to obtain the Figure 4 of the subsection 3.3 of Epifanio et al. (2013).

Value

A device with the desired plot.

Note

There are no usage and arguments sections in this help file because they are the same than those of the page 25 of the reference manual of archetypes.

Author(s)

Irene Epifanio

References

Epifanio, I., Vinue, G., and Alemany, S., (2013). Archetypal analysis: contributions for estimating boundary cases in multivariate accommodation problem, Computers & Industrial Engineering 64, 757–765.

See Also

archetypesBoundary, USAFSurvey

Examples

#First,the USAF 1967 database is read and preprocessed (Zehner et al. (1993)).
#Variable selection:
variabl_sel <- c(48, 40, 39, 33, 34, 36)
#Changing to inches: 
USAFSurvey_inch <- USAFSurvey[1:25, variabl_sel] / (10 * 2.54)

#Data preprocessing:
USAFSurvey_preproc <- preprocessing(USAFSurvey_inch, TRUE, 0.95, TRUE)

#Procedure and results shown in section 2.2.2 and section 3.1:
#For reproducing results, seed for randomness:
#suppressWarnings(RNGversion("3.5.0"))
#set.seed(2010)
res <- archetypesBoundary(USAFSurvey_preproc$data, 15, FALSE, 3)
#To understand the warning messages, see the vignette of the
#archetypes package.  

a3 <- archetypes::bestModel(res[[3]])
a7 <- archetypes::bestModel(res[[7]])

pznueva <- prcomp(USAFSurvey_preproc$data, scale = TRUE, retx = TRUE) 
#PCA scores for 3 archetypes:
p3 <- predict(pznueva,archetypes::parameters(a3)) 
#PCA scores for 7 archetypes:
p7 <- predict(pznueva,archetypes::parameters(a7))
#Representing the scores:
#Figure 4 (a):
xyplotPCArchetypes(p3[,1:2], pznueva$x[,1:2], data.col = gray(0.7), 
                   atypes.col = 1, atypes.pch = 15)
#Figure 4 (b):
xyplotPCArchetypes(p7[,1:2], pznueva$x[,1:2], data.col = gray(0.7), 
                   atypes.col = 1, atypes.pch = 15)