Anthropometry (version 1.19)

anthrCases: Helper generic function for obtaining the anthropometric cases

Description

Because the goal of the methodologies included in this package is always to estimate a number of anthropometric cases given a data set (both central (prototypes) and boundaries (archetypoids)), this auxiliary generic function allows the user to identify the cases computed by each method in an easy way.

Usage

anthrCases(resMethod, nsizes)
# S3 method for trimowa
anthrCases (resMethod, nsizes)
# S3 method for hipamAnthropom
anthrCases (resMethod, nsizes)

Value

A vector of class anthrCases with the anthropometric cases.

Arguments

resMethod

This is the object which saves the results obtained by the methodologies and which contains the anthropometric cases to return.

nsizes

Number of bust sizes. This argument is needed for the "trimowa" and "hipamAnthropom" methodologies because they can compute the prototypes for any given number of bust sizes.

Author

Guillermo Vinue

References

Vinue, G., Simo, A., and Alemany, S., (2016). The k-means algorithm for 3D shapes with an application to apparel design, Advances in Data Analysis and Classification 10(1), 103--132.

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.

Vinue, G., Leon, T., Alemany, S., and Ayala, G., (2014). Looking for representative fit models for apparel sizing, Decision Support Systems 57, 22--33.

Ibanez, M. V., Vinue, G., Alemany, S., Simo, A., Epifanio, I., Domingo, J., and Ayala, G., (2012). Apparel sizing using trimmed PAM and OWA operators, Expert Systems with Applications 39, 10512--10520.

Vinue, G., and Ibanez, M. V., (2014). Data depth and Biclustering applied to anthropometric data. Exploring their utility in apparel design. Technical report.

See Also

trimowa, TDDclust, hipamAnthropom, LloydShapes, HartiganShapes, trimmedLloydShapes, archetypoids, stepArchetypoids

Examples

#kmeansProcrustes:
landmarksNoNa <- na.exclude(landmarksSampleSpaSurv)
dim(landmarksNoNa) 
#[1] 574 198 
numLandmarks <- (dim(landmarksNoNa)[2]) / 3
#[1] 66
#As a toy example, only the first 10 individuals are used.
landmarksNoNa_First10 <- landmarksNoNa[1:10, ] 
(numIndiv <- dim(landmarksNoNa_First10)[1])
#[1] 10         
    
array3D <- array3Dlandm(numLandmarks, numIndiv, landmarksNoNa_First10)
#shapes::plotshapes(array3D[,,1]) 
#calibrate::textxy(array3D[,1,1], array3D[,2,1], labs = 1:numLandmarks, cex = 0.7) 

numClust <- 2 ; algSteps <- 1 ; niter <- 1 ; stopCr <- 0.0001
resLL <- LloydShapes(array3D, numClust, algSteps, niter, stopCr, FALSE, FALSE)

prototypes <- anthrCases(resLL)