Anthropometry (version 1.19)

stepArchetypesRawData: Archetype algorithm to raw data

Description

This is a slight modification of the original stepArchetypes function of the archetypes R package to apply the archetype algorithm to raw data. The stepArchetypes function standardizes the data by default and this option is not always desired.

Usage

stepArchetypesRawData(data,numArch,numRep=3,verbose=TRUE)

Value

A list with numArch elements. Each element is a list of class attribute stepArchetypes with numRep elements.

Arguments

data

Data to obtain archetypes.

numArch

Number of archetypes to compute, from 1 to numArch.

numRep

For each numArch, run archetypes numRep times.

verbose

If TRUE, the progress during execution is shown.

Author

Guillermo Vinue based on the the original stepArchetypes function of archetypes.

References

Eugster, M. J., and Leisch, F., (2009). From Spider-Man to Hero - Archetypal Analysis in R, Journal of Statistical Software 30, 1--23, tools:::Rd_expr_doi("10.18637/jss.v030.i08").

Vinue, G., Epifanio, I., and Alemany, S., (2015). Archetypoids: a new approach to define representative archetypal data, Computational Statistics and Data Analysis 87, 102--115.

See Also

stepArchetypes

Examples

#COCKPIT DESIGN PROBLEM:
#As a toy example, only the first 25 individuals are used.
USAFSurvey_First25 <- USAFSurvey[1:25, ]
#Variable selection:
variabl_sel <- c(48, 40, 39, 33, 34, 36)
#Changing to inches: 
USAFSurvey_First25_inch <- USAFSurvey_First25[,variabl_sel] / (10 * 2.54)

#Data preprocessing:
USAFSurvey_preproc <- preprocessing(USAFSurvey_First25_inch, TRUE, 0.95, TRUE)

#For reproducing results, seed for randomness:
#suppressWarnings(RNGversion("3.5.0"))
#set.seed(2010) 
#Run archetype algorithm repeatedly from 1 to numArch archetypes:
#This is a toy example. In other situation, choose numArch=10 and numRep=20.
numArch <- 5 ; numRep <- 2
lass <- stepArchetypesRawData(data = USAFSurvey_preproc$data, numArch = 1:numArch,
                              numRep = numRep, verbose = FALSE) 
#To understand the warning messages, see the vignette of the
#archetypes package.